Overview

Dataset statistics

Number of variables71
Number of observations13897
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 MiB
Average record size in memory568.0 B

Variable types

Categorical44
Numeric27

Alerts

year has constant value "2024"Constant
event is highly overall correlated with event_lag_1 and 9 other fieldsHigh correlation
event_lag_1 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lag_2 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lag_3 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lag_4 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lag_5 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lead_1 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lead_2 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lead_3 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lead_4 is highly overall correlated with event and 9 other fieldsHigh correlation
event_lead_5 is highly overall correlated with event and 9 other fieldsHigh correlation
green_market is highly overall correlated with specialities_marketHigh correlation
holiday is highly overall correlated with holiday_lag_1 and 9 other fieldsHigh correlation
holiday_lag_1 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lag_2 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lag_3 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lag_4 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lag_5 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lead_1 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lead_2 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lead_3 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lead_4 is highly overall correlated with holiday and 9 other fieldsHigh correlation
holiday_lead_5 is highly overall correlated with holiday and 9 other fieldsHigh correlation
hour is highly overall correlated with pedestrians_count_lag_1 and 4 other fieldsHigh correlation
humidity is highly overall correlated with humidity_lag_1 and 9 other fieldsHigh correlation
humidity_lag_1 is highly overall correlated with humidity and 8 other fieldsHigh correlation
humidity_lag_2 is highly overall correlated with humidity and 7 other fieldsHigh correlation
humidity_lag_3 is highly overall correlated with humidity and 6 other fieldsHigh correlation
humidity_lag_4 is highly overall correlated with humidity and 5 other fieldsHigh correlation
humidity_lag_5 is highly overall correlated with humidity and 4 other fieldsHigh correlation
month is highly overall correlated with temp and 5 other fieldsHigh correlation
pedestrians_count_lag_1 is highly overall correlated with hour and 6 other fieldsHigh correlation
pedestrians_count_lag_2 is highly overall correlated with hour and 7 other fieldsHigh correlation
pedestrians_count_lag_3 is highly overall correlated with hour and 8 other fieldsHigh correlation
pedestrians_count_lag_4 is highly overall correlated with hour and 9 other fieldsHigh correlation
pedestrians_count_lag_5 is highly overall correlated with hour and 10 other fieldsHigh correlation
precip is highly overall correlated with precip_lag_1 and 4 other fieldsHigh correlation
precip_lag_1 is highly overall correlated with precip and 4 other fieldsHigh correlation
precip_lag_2 is highly overall correlated with precip and 4 other fieldsHigh correlation
precip_lag_3 is highly overall correlated with precip and 4 other fieldsHigh correlation
precip_lag_4 is highly overall correlated with precip and 4 other fieldsHigh correlation
precip_lag_5 is highly overall correlated with precip and 4 other fieldsHigh correlation
series is highly overall correlated with pedestrians_count_lag_1 and 4 other fieldsHigh correlation
specialities_market is highly overall correlated with green_marketHigh correlation
temp is highly overall correlated with month and 5 other fieldsHigh correlation
temp_lag_1 is highly overall correlated with month and 5 other fieldsHigh correlation
temp_lag_2 is highly overall correlated with month and 5 other fieldsHigh correlation
temp_lag_3 is highly overall correlated with month and 5 other fieldsHigh correlation
temp_lag_4 is highly overall correlated with month and 5 other fieldsHigh correlation
temp_lag_5 is highly overall correlated with month and 5 other fieldsHigh correlation
weekday_Saturday is highly overall correlated with workday and 10 other fieldsHigh correlation
weekday_Sunday is highly overall correlated with workday and 10 other fieldsHigh correlation
workday is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lag_1 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lag_2 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lag_3 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lag_4 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lag_5 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lead_1 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lead_2 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lead_3 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lead_4 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
workday_lead_5 is highly overall correlated with weekday_Saturday and 11 other fieldsHigh correlation
holiday is highly imbalanced (66.6%)Imbalance
holiday_lag_1 is highly imbalanced (66.6%)Imbalance
holiday_lag_2 is highly imbalanced (66.6%)Imbalance
holiday_lag_3 is highly imbalanced (66.5%)Imbalance
holiday_lag_4 is highly imbalanced (66.5%)Imbalance
holiday_lag_5 is highly imbalanced (66.5%)Imbalance
holiday_lead_1 is highly imbalanced (66.7%)Imbalance
holiday_lead_2 is highly imbalanced (66.7%)Imbalance
holiday_lead_3 is highly imbalanced (66.7%)Imbalance
holiday_lead_4 is highly imbalanced (66.7%)Imbalance
holiday_lead_5 is highly imbalanced (66.8%)Imbalance
hour is uniformly distributedUniform
hour has 572 (4.1%) zerosZeros
series has 318 (2.3%) zerosZeros

Reproduction

Analysis started2024-07-14 16:18:39.785060
Analysis finished2024-07-14 16:20:54.830848
Duration2 minutes and 15.05 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

holiday
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2888298259458512
13041 
3.462246313119602
 
856

Length

Max length19
Median length19
Mean length18.876808
Min length17

Characters and Unicode

Total characters262331
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.462246313119602
2nd row3.462246313119602
3rd row3.462246313119602
4th row3.462246313119602
5th row3.462246313119602

Common Values

ValueCountFrequency (%)
-0.2888298259458512 13041
93.8%
3.462246313119602 856
 
6.2%

Length

2024-07-14T18:20:54.961501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:55.128053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2888298259458512 13041
93.8%
3.462246313119602 856
 
6.2%

Most occurring characters

ValueCountFrequency (%)
8 65205
24.9%
2 54732
20.9%
5 39123
14.9%
9 26938
10.3%
1 15609
 
6.0%
4 14753
 
5.6%
0 13897
 
5.3%
. 13897
 
5.3%
- 13041
 
5.0%
3 2568
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 65205
24.9%
2 54732
20.9%
5 39123
14.9%
9 26938
10.3%
1 15609
 
6.0%
4 14753
 
5.6%
0 13897
 
5.3%
. 13897
 
5.3%
- 13041
 
5.0%
3 2568
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 65205
24.9%
2 54732
20.9%
5 39123
14.9%
9 26938
10.3%
1 15609
 
6.0%
4 14753
 
5.6%
0 13897
 
5.3%
. 13897
 
5.3%
- 13041
 
5.0%
3 2568
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 65205
24.9%
2 54732
20.9%
5 39123
14.9%
9 26938
10.3%
1 15609
 
6.0%
4 14753
 
5.6%
0 13897
 
5.3%
. 13897
 
5.3%
- 13041
 
5.0%
3 2568
 
1.0%

workday
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10005 
-1.591758015261153
3892 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10005
72.0%
-1.591758015261153 3892
 
28.0%

Length

2024-07-14T18:20:55.277697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:55.403317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10005
72.0%
1.591758015261153 3892
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43912
17.6%
1 39470
15.8%
5 25573
10.2%
6 23902
9.6%
2 23902
9.6%
9 23902
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43912
17.6%
1 39470
15.8%
5 25573
10.2%
6 23902
9.6%
2 23902
9.6%
9 23902
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43912
17.6%
1 39470
15.8%
5 25573
10.2%
6 23902
9.6%
2 23902
9.6%
9 23902
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43912
17.6%
1 39470
15.8%
5 25573
10.2%
6 23902
9.6%
2 23902
9.6%
9 23902
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

green_market
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.8716962551528071
7958 
-1.1471885924583922
5939 

Length

Max length19
Median length18
Mean length18.427358
Min length18

Characters and Unicode

Total characters256085
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.1471885924583922
2nd row-1.1471885924583922
3rd row-1.1471885924583922
4th row-1.1471885924583922
5th row-1.1471885924583922

Common Values

ValueCountFrequency (%)
0.8716962551528071 7958
57.3%
-1.1471885924583922 5939
42.7%

Length

2024-07-14T18:20:55.552918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:55.672640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.8716962551528071 7958
57.3%
1.1471885924583922 5939
42.7%

Most occurring characters

ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 256085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 256085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 256085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

specialities_market
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.8716962551528071
7958 
-1.1471885924583922
5939 

Length

Max length19
Median length18
Mean length18.427358
Min length18

Characters and Unicode

Total characters256085
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.1471885924583922
2nd row-1.1471885924583922
3rd row-1.1471885924583922
4th row-1.1471885924583922
5th row-1.1471885924583922

Common Values

ValueCountFrequency (%)
0.8716962551528071 7958
57.3%
-1.1471885924583922 5939
42.7%

Length

2024-07-14T18:20:55.817211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:55.938884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.8716962551528071 7958
57.3%
1.1471885924583922 5939
42.7%

Most occurring characters

ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 256085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 256085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 256085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 41691
16.3%
5 35752
14.0%
8 33733
13.2%
2 33733
13.2%
7 21855
8.5%
9 19836
7.7%
0 15916
 
6.2%
6 15916
 
6.2%
. 13897
 
5.4%
4 11878
 
4.6%
Other values (2) 11878
 
4.6%

event
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4304736950032777
11160 
2.323022316131037
2737 

Length

Max length19
Median length19
Mean length18.606102
Min length17

Characters and Unicode

Total characters258569
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4304736950032777
2nd row-0.4304736950032777
3rd row-0.4304736950032777
4th row-0.4304736950032777
5th row-0.4304736950032777

Common Values

ValueCountFrequency (%)
-0.4304736950032777 11160
80.3%
2.323022316131037 2737
 
19.7%

Length

2024-07-14T18:20:56.183234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:56.335823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4304736950032777 11160
80.3%
2.323022316131037 2737
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 50114
19.4%
7 47377
18.3%
3 47165
18.2%
4 22320
8.6%
2 22108
8.6%
. 13897
 
5.4%
6 13897
 
5.4%
- 11160
 
4.3%
9 11160
 
4.3%
5 11160
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50114
19.4%
7 47377
18.3%
3 47165
18.2%
4 22320
8.6%
2 22108
8.6%
. 13897
 
5.4%
6 13897
 
5.4%
- 11160
 
4.3%
9 11160
 
4.3%
5 11160
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50114
19.4%
7 47377
18.3%
3 47165
18.2%
4 22320
8.6%
2 22108
8.6%
. 13897
 
5.4%
6 13897
 
5.4%
- 11160
 
4.3%
9 11160
 
4.3%
5 11160
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50114
19.4%
7 47377
18.3%
3 47165
18.2%
4 22320
8.6%
2 22108
8.6%
. 13897
 
5.4%
6 13897
 
5.4%
- 11160
 
4.3%
9 11160
 
4.3%
5 11160
 
4.3%

precip
Real number (ℝ)

HIGH CORRELATION 

Distinct404
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010804476
Minimum-0.19875046
Maximum35.186123
Zeros0
Zeros (%)0.0%
Negative12284
Negative (%)88.4%
Memory size108.7 KiB
2024-07-14T18:20:56.486463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.19875046
5-th percentile-0.19875046
Q1-0.19875046
median-0.19875046
Q3-0.19875046
95-th percentile0.88950753
Maximum35.186123
Range35.384873
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1736181
Coefficient of variation (CV)108.62332
Kurtosis297.56151
Mean0.010804476
Median Absolute Deviation (MAD)0
Skewness14.117775
Sum150.14981
Variance1.3773794
MonotonicityNot monotonic
2024-07-14T18:20:56.662948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1943534597 300
 
2.2%
-0.1965519606 213
 
1.5%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1855594557 69
 
0.5%
-0.1877579567 66
 
0.5%
-0.1833609547 54
 
0.4%
0.01010713218 54
 
0.4%
-0.1789639527 36
 
0.3%
Other values (394) 1949
 
14.0%
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1965519606 213
 
1.5%
-0.1943534597 300
 
2.2%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1877579567 66
 
0.5%
-0.1855594557 69
 
0.5%
-0.1833609547 54
 
0.4%
-0.1811624537 36
 
0.3%
-0.1789639527 36
 
0.3%
ValueCountFrequency (%)
35.18612293 3
< 0.1%
26.04695433 3
< 0.1%
22.98004545 3
< 0.1%
16.31858746 3
< 0.1%
15.46556907 3
< 0.1%
15.01267787 3
< 0.1%
13.9683899 3
< 0.1%
13.94420639 3
< 0.1%
12.39206469 3
< 0.1%
11.31919621 3
< 0.1%

pedestrians_count_lag_1
Real number (ℝ)

HIGH CORRELATION 

Distinct2906
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015209207
Minimum-0.84409036
Maximum6.000698
Zeros0
Zeros (%)0.0%
Negative8654
Negative (%)62.3%
Memory size108.7 KiB
2024-07-14T18:20:56.847455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.84409036
5-th percentile-0.83751936
Q1-0.78385622
median-0.36331243
Q30.54896097
95-th percentile1.9095954
Maximum6.000698
Range6.8447884
Interquartile range (IQR)1.3328172

Descriptive statistics

Standard deviation0.99422676
Coefficient of variation (CV)65.370059
Kurtosis2.7471879
Mean0.015209207
Median Absolute Deviation (MAD)0.45668428
Skewness1.540239
Sum211.36235
Variance0.98848685
MonotonicityNot monotonic
2024-07-14T18:20:57.023982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8440903612 318
 
2.3%
-0.8375193643 125
 
0.9%
-0.8364241982 113
 
0.8%
-0.8342338659 112
 
0.8%
-0.8353290321 103
 
0.7%
-0.8309483675 101
 
0.7%
-0.826567703 92
 
0.7%
-0.8298532014 89
 
0.6%
-0.8320435336 86
 
0.6%
-0.8397096966 85
 
0.6%
Other values (2896) 12673
91.2%
ValueCountFrequency (%)
-0.8440903612 318
2.3%
-0.842995195 20
 
0.1%
-0.8419000289 36
 
0.3%
-0.8408048628 62
 
0.4%
-0.8397096966 85
 
0.6%
-0.8386145305 80
 
0.6%
-0.8375193643 125
 
0.9%
-0.8364241982 113
 
0.8%
-0.8353290321 103
 
0.7%
-0.8342338659 112
 
0.8%
ValueCountFrequency (%)
6.000698004 1
< 0.1%
5.561536383 1
< 0.1%
5.528681398 1
< 0.1%
5.502397411 1
< 0.1%
5.312933669 1
< 0.1%
5.307457838 1
< 0.1%
5.272412522 1
< 0.1%
5.190275062 1
< 0.1%
5.188084729 1
< 0.1%
5.186989563 1
< 0.1%

pedestrians_count_lag_2
Real number (ℝ)

HIGH CORRELATION 

Distinct2906
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015114002
Minimum-0.8441593
Maximum6.0007431
Zeros0
Zeros (%)0.0%
Negative8654
Negative (%)62.3%
Memory size108.7 KiB
2024-07-14T18:20:57.204499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8441593
5-th percentile-0.83758819
Q1-0.78392416
median-0.36337335
Q30.54891524
95-th percentile1.9095723
Maximum6.0007431
Range6.8449024
Interquartile range (IQR)1.3328394

Descriptive statistics

Standard deviation0.99423238
Coefficient of variation (CV)65.782204
Kurtosis2.7476859
Mean0.015114002
Median Absolute Deviation (MAD)0.45669189
Skewness1.5404001
Sum210.03929
Variance0.98849802
MonotonicityNot monotonic
2024-07-14T18:20:57.406960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8441592982 318
 
2.3%
-0.8375881919 125
 
0.9%
-0.8364930075 113
 
0.8%
-0.8343026387 112
 
0.8%
-0.8353978231 103
 
0.7%
-0.8310170856 101
 
0.7%
-0.826636348 92
 
0.7%
-0.8299219012 89
 
0.6%
-0.83211227 86
 
0.6%
-0.8397785607 85
 
0.6%
Other values (2896) 12673
91.2%
ValueCountFrequency (%)
-0.8441592982 318
2.3%
-0.8430641138 20
 
0.1%
-0.8419689294 36
 
0.3%
-0.8408737451 62
 
0.4%
-0.8397785607 85
 
0.6%
-0.8386833763 80
 
0.6%
-0.8375881919 125
 
0.9%
-0.8364930075 113
 
0.8%
-0.8353978231 103
 
0.7%
-0.8343026387 112
 
0.8%
ValueCountFrequency (%)
6.000743117 1
< 0.1%
5.561574178 1
< 0.1%
5.528718647 1
< 0.1%
5.502434221 1
< 0.1%
5.312967323 1
< 0.1%
5.307491401 1
< 0.1%
5.2724455 1
< 0.1%
5.190306671 1
< 0.1%
5.188116303 1
< 0.1%
5.187021118 1
< 0.1%

pedestrians_count_lag_3
Real number (ℝ)

HIGH CORRELATION 

Distinct2906
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015019449
Minimum-0.84427975
Maximum6.0007223
Zeros0
Zeros (%)0.0%
Negative8654
Negative (%)62.3%
Memory size108.7 KiB
2024-07-14T18:20:57.599443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.84427975
5-th percentile-0.83770855
Q1-0.78404374
median-0.36348681
Q30.54881506
95-th percentile1.9094919
Maximum6.0007223
Range6.845002
Interquartile range (IQR)1.3328588

Descriptive statistics

Standard deviation0.99425168
Coefficient of variation (CV)66.197613
Kurtosis2.7474938
Mean0.015019449
Median Absolute Deviation (MAD)0.45669854
Skewness1.5403426
Sum208.72528
Variance0.9885364
MonotonicityNot monotonic
2024-07-14T18:20:57.782953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8442797534 318
 
2.3%
-0.8377085514 125
 
0.9%
-0.8366133511 113
 
0.8%
-0.8344229505 112
 
0.8%
-0.8355181508 103
 
0.7%
-0.8311373495 101
 
0.7%
-0.8267565482 92
 
0.7%
-0.8300421492 89
 
0.6%
-0.8322325498 86
 
0.6%
-0.8398989521 85
 
0.6%
Other values (2896) 12673
91.2%
ValueCountFrequency (%)
-0.8442797534 318
2.3%
-0.843184553 20
 
0.1%
-0.8420893527 36
 
0.3%
-0.8409941524 62
 
0.4%
-0.8398989521 85
 
0.6%
-0.8388037517 80
 
0.6%
-0.8377085514 125
 
0.9%
-0.8366133511 113
 
0.8%
-0.8355181508 103
 
0.7%
-0.8344229505 112
 
0.8%
ValueCountFrequency (%)
6.000722274 1
< 0.1%
5.561546944 1
< 0.1%
5.528690934 1
< 0.1%
5.502406126 1
< 0.1%
5.31293647 1
< 0.1%
5.307460469 1
< 0.1%
5.272414058 1
< 0.1%
5.190274034 1
< 0.1%
5.188083633 1
< 0.1%
5.186988433 1
< 0.1%

pedestrians_count_lag_4
Real number (ℝ)

HIGH CORRELATION 

Distinct2906
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014870467
Minimum-0.84433054
Maximum6.0008109
Zeros0
Zeros (%)0.0%
Negative8655
Negative (%)62.3%
Memory size108.7 KiB
2024-07-14T18:20:57.956488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.84433054
5-th percentile-0.8377592
Q1-0.78409329
median-0.36462303
Q30.54879264
95-th percentile1.9094972
Maximum6.0008109
Range6.8451414
Interquartile range (IQR)1.3328859

Descriptive statistics

Standard deviation0.99422891
Coefficient of variation (CV)66.859292
Kurtosis2.7490687
Mean0.014870467
Median Absolute Deviation (MAD)0.45561261
Skewness1.5407673
Sum206.65488
Variance0.98849113
MonotonicityNot monotonic
2024-07-14T18:20:58.128030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8443305388 318
 
2.3%
-0.8377592031 125
 
0.9%
-0.8366639804 113
 
0.8%
-0.8344735352 112
 
0.8%
-0.8355687578 103
 
0.7%
-0.8311878673 101
 
0.7%
-0.8268069768 92
 
0.7%
-0.8300926447 89
 
0.6%
-0.8322830899 86
 
0.6%
-0.8399496483 85
 
0.6%
Other values (2896) 12673
91.2%
ValueCountFrequency (%)
-0.8443305388 318
2.3%
-0.8432353162 20
 
0.1%
-0.8421400936 36
 
0.3%
-0.8410448709 62
 
0.4%
-0.8399496483 85
 
0.6%
-0.8388544257 80
 
0.6%
-0.8377592031 125
 
0.9%
-0.8366639804 113
 
0.8%
-0.8355687578 103
 
0.7%
-0.8344735352 112
 
0.8%
ValueCountFrequency (%)
6.000810863 1
< 0.1%
5.561626591 1
< 0.1%
5.528769912 1
< 0.1%
5.502484569 1
< 0.1%
5.313011055 1
< 0.1%
5.307534942 1
< 0.1%
5.272487818 1
< 0.1%
5.190346121 1
< 0.1%
5.188155676 1
< 0.1%
5.187060454 1
< 0.1%

pedestrians_count_lag_5
Real number (ℝ)

HIGH CORRELATION 

Distinct2906
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014790898
Minimum-0.84446438
Maximum6.000739
Zeros0
Zeros (%)0.0%
Negative8659
Negative (%)62.3%
Memory size108.7 KiB
2024-07-14T18:20:58.306552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.84446438
5-th percentile-0.83789299
Q1-0.78422659
median-0.36475253
Q30.5486714
95-th percentile1.9093883
Maximum6.000739
Range6.8452034
Interquartile range (IQR)1.332898

Descriptive statistics

Standard deviation0.99425156
Coefficient of variation (CV)67.220502
Kurtosis2.7484752
Mean0.014790898
Median Absolute Deviation (MAD)0.45561674
Skewness1.5405806
Sum205.54911
Variance0.98853617
MonotonicityNot monotonic
2024-07-14T18:20:58.486113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8444643829 318
 
2.3%
-0.8378929877 125
 
0.9%
-0.8367977552 113
 
0.8%
-0.8346072901 112
 
0.8%
-0.8357025226 103
 
0.7%
-0.8313215925 101
 
0.7%
-0.8269406623 92
 
0.7%
-0.8302263599 89
 
0.6%
-0.832416825 86
 
0.6%
-0.8400834528 85
 
0.6%
Other values (2896) 12673
91.2%
ValueCountFrequency (%)
-0.8444643829 318
2.3%
-0.8433691504 20
 
0.1%
-0.8422739178 36
 
0.3%
-0.8411786853 62
 
0.4%
-0.8400834528 85
 
0.6%
-0.8389882202 80
 
0.6%
-0.8378929877 125
 
0.9%
-0.8367977552 113
 
0.8%
-0.8357025226 103
 
0.7%
-0.8346072901 112
 
0.8%
ValueCountFrequency (%)
6.000738971 1
< 0.1%
5.561550724 1
< 0.1%
5.528693748 1
< 0.1%
5.502408167 1
< 0.1%
5.312932938 1
< 0.1%
5.307456776 1
< 0.1%
5.272409335 1
< 0.1%
5.190266894 1
< 0.1%
5.188076429 1
< 0.1%
5.186981197 1
< 0.1%

event_lag_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4303268951317986
11161 
2.323814782001308
2736 

Length

Max length19
Median length19
Mean length18.606246
Min length17

Characters and Unicode

Total characters258571
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4303268951317986
2nd row-0.4303268951317986
3rd row-0.4303268951317986
4th row-0.4303268951317986
5th row-0.4303268951317986

Common Values

ValueCountFrequency (%)
-0.4303268951317986 11161
80.3%
2.323814782001308 2736
 
19.7%

Length

2024-07-14T18:20:58.662601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:58.806216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4303268951317986 11161
80.3%
2.323814782001308 2736
 
19.7%

Most occurring characters

ValueCountFrequency (%)
3 41691
16.1%
0 30530
11.8%
8 30530
11.8%
1 27794
10.7%
6 22322
8.6%
9 22322
8.6%
2 19369
7.5%
. 13897
 
5.4%
4 13897
 
5.4%
7 13897
 
5.4%
Other values (2) 22322
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 41691
16.1%
0 30530
11.8%
8 30530
11.8%
1 27794
10.7%
6 22322
8.6%
9 22322
8.6%
2 19369
7.5%
. 13897
 
5.4%
4 13897
 
5.4%
7 13897
 
5.4%
Other values (2) 22322
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 41691
16.1%
0 30530
11.8%
8 30530
11.8%
1 27794
10.7%
6 22322
8.6%
9 22322
8.6%
2 19369
7.5%
. 13897
 
5.4%
4 13897
 
5.4%
7 13897
 
5.4%
Other values (2) 22322
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 41691
16.1%
0 30530
11.8%
8 30530
11.8%
1 27794
10.7%
6 22322
8.6%
9 22322
8.6%
2 19369
7.5%
. 13897
 
5.4%
4 13897
 
5.4%
7 13897
 
5.4%
Other values (2) 22322
8.6%

holiday_lag_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2890125691163145
13040 
3.4600571285103703
 
857

Length

Max length19
Median length19
Mean length18.938332
Min length18

Characters and Unicode

Total characters263186
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.4600571285103703
2nd row3.4600571285103703
3rd row3.4600571285103703
4th row3.4600571285103703
5th row3.4600571285103703

Common Values

ValueCountFrequency (%)
-0.2890125691163145 13040
93.8%
3.4600571285103703 857
 
6.2%

Length

2024-07-14T18:20:58.975762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:59.189192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2890125691163145 13040
93.8%
3.4600571285103703 857
 
6.2%

Most occurring characters

ValueCountFrequency (%)
1 53874
20.5%
0 29508
11.2%
5 27794
10.6%
2 26937
10.2%
6 26937
10.2%
9 26080
9.9%
3 15611
 
5.9%
. 13897
 
5.3%
8 13897
 
5.3%
4 13897
 
5.3%
Other values (2) 14754
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 263186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 53874
20.5%
0 29508
11.2%
5 27794
10.6%
2 26937
10.2%
6 26937
10.2%
9 26080
9.9%
3 15611
 
5.9%
. 13897
 
5.3%
8 13897
 
5.3%
4 13897
 
5.3%
Other values (2) 14754
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 263186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 53874
20.5%
0 29508
11.2%
5 27794
10.6%
2 26937
10.2%
6 26937
10.2%
9 26080
9.9%
3 15611
 
5.9%
. 13897
 
5.3%
8 13897
 
5.3%
4 13897
 
5.3%
Other values (2) 14754
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 263186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 53874
20.5%
0 29508
11.2%
5 27794
10.6%
2 26937
10.2%
6 26937
10.2%
9 26080
9.9%
3 15611
 
5.9%
. 13897
 
5.3%
8 13897
 
5.3%
4 13897
 
5.3%
Other values (2) 14754
 
5.6%

workday_lag_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10006 
-1.591758015261153
3891 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10006
72.0%
-1.591758015261153 3891
 
28.0%

Length

2024-07-14T18:20:59.393645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:59.563192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10006
72.0%
1.591758015261153 3891
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43915
17.6%
1 39467
15.8%
5 25570
10.2%
6 23903
9.6%
2 23903
9.6%
9 23903
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43915
17.6%
1 39467
15.8%
5 25570
10.2%
6 23903
9.6%
2 23903
9.6%
9 23903
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43915
17.6%
1 39467
15.8%
5 25570
10.2%
6 23903
9.6%
2 23903
9.6%
9 23903
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43915
17.6%
1 39467
15.8%
5 25570
10.2%
6 23903
9.6%
2 23903
9.6%
9 23903
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lag_2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.430180076477847
11162 
2.324607890229656
2735 

Length

Max length18
Median length18
Mean length17.803195
Min length17

Characters and Unicode

Total characters247411
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.430180076477847
2nd row-0.430180076477847
3rd row-0.430180076477847
4th row-0.430180076477847
5th row-0.430180076477847

Common Values

ValueCountFrequency (%)
-0.430180076477847 11162
80.3%
2.324607890229656 2735
 
19.7%

Length

2024-07-14T18:20:59.742711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:20:59.922230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.430180076477847 11162
80.3%
2.324607890229656 2735
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 50118
20.3%
7 47383
19.2%
4 36221
14.6%
8 25059
10.1%
6 19367
 
7.8%
. 13897
 
5.6%
3 13897
 
5.6%
- 11162
 
4.5%
1 11162
 
4.5%
2 10940
 
4.4%
Other values (2) 8205
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 247411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50118
20.3%
7 47383
19.2%
4 36221
14.6%
8 25059
10.1%
6 19367
 
7.8%
. 13897
 
5.6%
3 13897
 
5.6%
- 11162
 
4.5%
1 11162
 
4.5%
2 10940
 
4.4%
Other values (2) 8205
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 247411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50118
20.3%
7 47383
19.2%
4 36221
14.6%
8 25059
10.1%
6 19367
 
7.8%
. 13897
 
5.6%
3 13897
 
5.6%
- 11162
 
4.5%
1 11162
 
4.5%
2 10940
 
4.4%
Other values (2) 8205
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 247411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50118
20.3%
7 47383
19.2%
4 36221
14.6%
8 25059
10.1%
6 19367
 
7.8%
. 13897
 
5.6%
3 13897
 
5.6%
- 11162
 
4.5%
1 11162
 
4.5%
2 10940
 
4.4%
Other values (2) 8205
 
3.3%

holiday_lag_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2891952324030994
13039 
3.457871665761535
 
858

Length

Max length19
Median length19
Mean length18.87652
Min length17

Characters and Unicode

Total characters262327
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.457871665761535
2nd row3.457871665761535
3rd row3.457871665761535
4th row3.457871665761535
5th row3.457871665761535

Common Values

ValueCountFrequency (%)
-0.2891952324030994 13039
93.8%
3.457871665761535 858
 
6.2%

Length

2024-07-14T18:21:00.099756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:00.309197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2891952324030994 13039
93.8%
3.457871665761535 858
 
6.2%

Most occurring characters

ValueCountFrequency (%)
9 52156
19.9%
0 39117
14.9%
2 39117
14.9%
3 27794
10.6%
4 26936
10.3%
5 16471
 
6.3%
1 14755
 
5.6%
. 13897
 
5.3%
8 13897
 
5.3%
- 13039
 
5.0%
Other values (2) 5148
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 52156
19.9%
0 39117
14.9%
2 39117
14.9%
3 27794
10.6%
4 26936
10.3%
5 16471
 
6.3%
1 14755
 
5.6%
. 13897
 
5.3%
8 13897
 
5.3%
- 13039
 
5.0%
Other values (2) 5148
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 52156
19.9%
0 39117
14.9%
2 39117
14.9%
3 27794
10.6%
4 26936
10.3%
5 16471
 
6.3%
1 14755
 
5.6%
. 13897
 
5.3%
8 13897
 
5.3%
- 13039
 
5.0%
Other values (2) 5148
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 52156
19.9%
0 39117
14.9%
2 39117
14.9%
3 27794
10.6%
4 26936
10.3%
5 16471
 
6.3%
1 14755
 
5.6%
. 13897
 
5.3%
8 13897
 
5.3%
- 13039
 
5.0%
Other values (2) 5148
 
2.0%

workday_lag_2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10007 
-1.591758015261153
3890 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10007
72.0%
-1.591758015261153 3890
 
28.0%

Length

2024-07-14T18:21:00.515645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:00.679244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10007
72.0%
1.591758015261153 3890
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43918
17.6%
1 39464
15.8%
5 25567
10.2%
6 23904
9.6%
2 23904
9.6%
9 23904
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43918
17.6%
1 39464
15.8%
5 25567
10.2%
6 23904
9.6%
2 23904
9.6%
9 23904
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43918
17.6%
1 39464
15.8%
5 25567
10.2%
6 23904
9.6%
2 23904
9.6%
9 23904
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43918
17.6%
1 39464
15.8%
5 25567
10.2%
6 23904
9.6%
2 23904
9.6%
9 23904
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lag_3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4300332390121691
11163 
2.3254016417361214
2734 

Length

Max length19
Median length19
Mean length18.803267
Min length18

Characters and Unicode

Total characters261309
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4300332390121691
2nd row-0.4300332390121691
3rd row-0.4300332390121691
4th row-0.4300332390121691
5th row-0.4300332390121691

Common Values

ValueCountFrequency (%)
-0.4300332390121691 11163
80.3%
2.3254016417361214 2734
 
19.7%

Length

2024-07-14T18:21:00.844766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:00.990377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4300332390121691 11163
80.3%
2.3254016417361214 2734
 
19.7%

Most occurring characters

ValueCountFrequency (%)
3 50120
19.2%
0 47386
18.1%
1 44425
17.0%
2 30528
11.7%
9 22326
8.5%
4 19365
 
7.4%
6 16631
 
6.4%
. 13897
 
5.3%
- 11163
 
4.3%
5 2734
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 50120
19.2%
0 47386
18.1%
1 44425
17.0%
2 30528
11.7%
9 22326
8.5%
4 19365
 
7.4%
6 16631
 
6.4%
. 13897
 
5.3%
- 11163
 
4.3%
5 2734
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 50120
19.2%
0 47386
18.1%
1 44425
17.0%
2 30528
11.7%
9 22326
8.5%
4 19365
 
7.4%
6 16631
 
6.4%
. 13897
 
5.3%
- 11163
 
4.3%
5 2734
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 50120
19.2%
0 47386
18.1%
1 44425
17.0%
2 30528
11.7%
9 22326
8.5%
4 19365
 
7.4%
6 16631
 
6.4%
. 13897
 
5.3%
- 11163
 
4.3%
5 2734
 
1.0%

holiday_lag_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.289377815967883
13038 
3.455689914084452
 
859

Length

Max length18
Median length18
Mean length17.938188
Min length17

Characters and Unicode

Total characters249287
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.455689914084452
2nd row3.455689914084452
3rd row3.455689914084452
4th row3.455689914084452
5th row3.455689914084452

Common Values

ValueCountFrequency (%)
-0.289377815967883 13038
93.8%
3.455689914084452 859
 
6.2%

Length

2024-07-14T18:21:01.148950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:01.285584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.289377815967883 13038
93.8%
3.455689914084452 859
 
6.2%

Most occurring characters

ValueCountFrequency (%)
8 53870
21.6%
7 39114
15.7%
9 27794
11.1%
3 26935
10.8%
5 15615
 
6.3%
0 13897
 
5.6%
. 13897
 
5.6%
2 13897
 
5.6%
1 13897
 
5.6%
6 13897
 
5.6%
Other values (2) 16474
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 53870
21.6%
7 39114
15.7%
9 27794
11.1%
3 26935
10.8%
5 15615
 
6.3%
0 13897
 
5.6%
. 13897
 
5.6%
2 13897
 
5.6%
1 13897
 
5.6%
6 13897
 
5.6%
Other values (2) 16474
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 53870
21.6%
7 39114
15.7%
9 27794
11.1%
3 26935
10.8%
5 15615
 
6.3%
0 13897
 
5.6%
. 13897
 
5.6%
2 13897
 
5.6%
1 13897
 
5.6%
6 13897
 
5.6%
Other values (2) 16474
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 53870
21.6%
7 39114
15.7%
9 27794
11.1%
3 26935
10.8%
5 15615
 
6.3%
0 13897
 
5.6%
. 13897
 
5.6%
2 13897
 
5.6%
1 13897
 
5.6%
6 13897
 
5.6%
Other values (2) 16474
 
6.6%

workday_lag_3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10008 
-1.591758015261153
3889 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10008
72.0%
-1.591758015261153 3889
 
28.0%

Length

2024-07-14T18:21:01.421222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:01.556858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10008
72.0%
1.591758015261153 3889
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43921
17.6%
1 39461
15.8%
5 25564
10.2%
6 23905
9.6%
2 23905
9.6%
9 23905
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43921
17.6%
1 39461
15.8%
5 25564
10.2%
6 23905
9.6%
2 23905
9.6%
9 23905
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43921
17.6%
1 39461
15.8%
5 25564
10.2%
6 23905
9.6%
2 23905
9.6%
9 23905
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43921
17.6%
1 39461
15.8%
5 25564
10.2%
6 23905
9.6%
2 23905
9.6%
9 23905
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lag_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4298863827054728
11164 
2.3261960374425903
2733 

Length

Max length19
Median length19
Mean length18.803339
Min length18

Characters and Unicode

Total characters261310
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4298863827054728
2nd row-0.4298863827054728
3rd row-0.4298863827054728
4th row-0.4298863827054728
5th row-0.4298863827054728

Common Values

ValueCountFrequency (%)
-0.4298863827054728 11164
80.3%
2.3261960374425903 2733
 
19.7%

Length

2024-07-14T18:21:01.712487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:01.844091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4298863827054728 11164
80.3%
2.3261960374425903 2733
 
19.7%

Most occurring characters

ValueCountFrequency (%)
8 44656
17.1%
2 41691
16.0%
0 27794
10.6%
4 27794
10.6%
7 25061
9.6%
3 19363
7.4%
9 16630
 
6.4%
6 16630
 
6.4%
. 13897
 
5.3%
5 13897
 
5.3%
Other values (2) 13897
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 44656
17.1%
2 41691
16.0%
0 27794
10.6%
4 27794
10.6%
7 25061
9.6%
3 19363
7.4%
9 16630
 
6.4%
6 16630
 
6.4%
. 13897
 
5.3%
5 13897
 
5.3%
Other values (2) 13897
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 44656
17.1%
2 41691
16.0%
0 27794
10.6%
4 27794
10.6%
7 25061
9.6%
3 19363
7.4%
9 16630
 
6.4%
6 16630
 
6.4%
. 13897
 
5.3%
5 13897
 
5.3%
Other values (2) 13897
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 44656
17.1%
2 41691
16.0%
0 27794
10.6%
4 27794
10.6%
7 25061
9.6%
3 19363
7.4%
9 16630
 
6.4%
6 16630
 
6.4%
. 13897
 
5.3%
5 13897
 
5.3%
Other values (2) 13897
 
5.3%

holiday_lag_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2895603199718723
13037 
3.453511862734296
 
860

Length

Max length19
Median length19
Mean length18.876232
Min length17

Characters and Unicode

Total characters262323
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.453511862734296
2nd row3.453511862734296
3rd row3.453511862734296
4th row3.453511862734296
5th row3.453511862734296

Common Values

ValueCountFrequency (%)
-0.2895603199718723 13037
93.8%
3.453511862734296 860
 
6.2%

Length

2024-07-14T18:21:01.996682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:02.149275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2895603199718723 13037
93.8%
3.453511862734296 860
 
6.2%

Most occurring characters

ValueCountFrequency (%)
9 39971
15.2%
3 28654
10.9%
2 27794
10.6%
1 27794
10.6%
8 26934
10.3%
7 26934
10.3%
0 26074
9.9%
5 14757
 
5.6%
6 14757
 
5.6%
. 13897
 
5.3%
Other values (2) 14757
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 39971
15.2%
3 28654
10.9%
2 27794
10.6%
1 27794
10.6%
8 26934
10.3%
7 26934
10.3%
0 26074
9.9%
5 14757
 
5.6%
6 14757
 
5.6%
. 13897
 
5.3%
Other values (2) 14757
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 39971
15.2%
3 28654
10.9%
2 27794
10.6%
1 27794
10.6%
8 26934
10.3%
7 26934
10.3%
0 26074
9.9%
5 14757
 
5.6%
6 14757
 
5.6%
. 13897
 
5.3%
Other values (2) 14757
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 39971
15.2%
3 28654
10.9%
2 27794
10.6%
1 27794
10.6%
8 26934
10.3%
7 26934
10.3%
0 26074
9.9%
5 14757
 
5.6%
6 14757
 
5.6%
. 13897
 
5.3%
Other values (2) 14757
 
5.6%

workday_lag_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10009 
-1.591758015261153
3888 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10009
72.0%
-1.591758015261153 3888
 
28.0%

Length

2024-07-14T18:21:02.273941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:02.389632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10009
72.0%
1.591758015261153 3888
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43924
17.6%
1 39458
15.8%
5 25561
10.2%
6 23906
9.6%
2 23906
9.6%
9 23906
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43924
17.6%
1 39458
15.8%
5 25561
10.2%
6 23906
9.6%
2 23906
9.6%
9 23906
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43924
17.6%
1 39458
15.8%
5 25561
10.2%
6 23906
9.6%
2 23906
9.6%
9 23906
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43924
17.6%
1 39458
15.8%
5 25561
10.2%
6 23906
9.6%
2 23906
9.6%
9 23906
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lag_5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4297395075284279
11165 
2.326991078272803
2732 

Length

Max length19
Median length19
Mean length18.606822
Min length17

Characters and Unicode

Total characters258579
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4297395075284279
2nd row-0.4297395075284279
3rd row-0.4297395075284279
4th row-0.4297395075284279
5th row-0.4297395075284279

Common Values

ValueCountFrequency (%)
-0.4297395075284279 11165
80.3%
2.326991078272803 2732
 
19.7%

Length

2024-07-14T18:21:02.537280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:02.692823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4297395075284279 11165
80.3%
2.326991078272803 2732
 
19.7%

Most occurring characters

ValueCountFrequency (%)
2 44423
17.2%
9 38959
15.1%
7 38959
15.1%
0 27794
10.7%
4 22330
8.6%
5 22330
8.6%
3 16629
 
6.4%
8 16629
 
6.4%
. 13897
 
5.4%
- 11165
 
4.3%
Other values (2) 5464
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 44423
17.2%
9 38959
15.1%
7 38959
15.1%
0 27794
10.7%
4 22330
8.6%
5 22330
8.6%
3 16629
 
6.4%
8 16629
 
6.4%
. 13897
 
5.4%
- 11165
 
4.3%
Other values (2) 5464
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 44423
17.2%
9 38959
15.1%
7 38959
15.1%
0 27794
10.7%
4 22330
8.6%
5 22330
8.6%
3 16629
 
6.4%
8 16629
 
6.4%
. 13897
 
5.4%
- 11165
 
4.3%
Other values (2) 5464
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 44423
17.2%
9 38959
15.1%
7 38959
15.1%
0 27794
10.7%
4 22330
8.6%
5 22330
8.6%
3 16629
 
6.4%
8 16629
 
6.4%
. 13897
 
5.4%
- 11165
 
4.3%
Other values (2) 5464
 
2.1%

holiday_lag_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2897427445758071
13036 
3.451337501009846
 
861

Length

Max length19
Median length19
Mean length18.876088
Min length17

Characters and Unicode

Total characters262321
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.451337501009846
2nd row3.451337501009846
3rd row3.451337501009846
4th row3.451337501009846
5th row3.451337501009846

Common Values

ValueCountFrequency (%)
-0.2897427445758071 13036
93.8%
3.451337501009846 861
 
6.2%

Length

2024-07-14T18:21:02.873337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:03.009973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2897427445758071 13036
93.8%
3.451337501009846 861
 
6.2%

Most occurring characters

ValueCountFrequency (%)
7 53005
20.2%
4 40830
15.6%
0 28655
10.9%
5 27794
10.6%
8 26933
10.3%
2 26072
9.9%
1 14758
 
5.6%
. 13897
 
5.3%
9 13897
 
5.3%
- 13036
 
5.0%
Other values (2) 3444
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 53005
20.2%
4 40830
15.6%
0 28655
10.9%
5 27794
10.6%
8 26933
10.3%
2 26072
9.9%
1 14758
 
5.6%
. 13897
 
5.3%
9 13897
 
5.3%
- 13036
 
5.0%
Other values (2) 3444
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 53005
20.2%
4 40830
15.6%
0 28655
10.9%
5 27794
10.6%
8 26933
10.3%
2 26072
9.9%
1 14758
 
5.6%
. 13897
 
5.3%
9 13897
 
5.3%
- 13036
 
5.0%
Other values (2) 3444
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 53005
20.2%
4 40830
15.6%
0 28655
10.9%
5 27794
10.6%
8 26933
10.3%
2 26072
9.9%
1 14758
 
5.6%
. 13897
 
5.3%
9 13897
 
5.3%
- 13036
 
5.0%
Other values (2) 3444
 
1.3%

workday_lag_5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10010 
-1.591758015261153
3887 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10010
72.0%
-1.591758015261153 3887
 
28.0%

Length

2024-07-14T18:21:03.158612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:03.288228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10010
72.0%
1.591758015261153 3887
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43927
17.6%
1 39455
15.8%
5 25558
10.2%
6 23907
9.6%
2 23907
9.6%
9 23907
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43927
17.6%
1 39455
15.8%
5 25558
10.2%
6 23907
9.6%
2 23907
9.6%
9 23907
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43927
17.6%
1 39455
15.8%
5 25558
10.2%
6 23907
9.6%
2 23907
9.6%
9 23907
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43927
17.6%
1 39455
15.8%
5 25558
10.2%
6 23907
9.6%
2 23907
9.6%
9 23907
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lead_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4306204761215005
11159 
2.3222304917006507
2738 

Length

Max length19
Median length19
Mean length18.802979
Min length18

Characters and Unicode

Total characters261305
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4306204761215005
2nd row-0.4306204761215005
3rd row-0.4306204761215005
4th row-0.4306204761215005
5th row-0.4306204761215005

Common Values

ValueCountFrequency (%)
-0.4306204761215005 11159
80.3%
2.3222304917006507 2738
 
19.7%

Length

2024-07-14T18:21:03.430847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:03.572469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4306204761215005 11159
80.3%
2.3222304917006507 2738
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 66747
25.5%
2 33270
12.7%
4 25056
 
9.6%
6 25056
 
9.6%
1 25056
 
9.6%
5 25056
 
9.6%
3 16635
 
6.4%
7 16635
 
6.4%
. 13897
 
5.3%
- 11159
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 66747
25.5%
2 33270
12.7%
4 25056
 
9.6%
6 25056
 
9.6%
1 25056
 
9.6%
5 25056
 
9.6%
3 16635
 
6.4%
7 16635
 
6.4%
. 13897
 
5.3%
- 11159
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 66747
25.5%
2 33270
12.7%
4 25056
 
9.6%
6 25056
 
9.6%
1 25056
 
9.6%
5 25056
 
9.6%
3 16635
 
6.4%
7 16635
 
6.4%
. 13897
 
5.3%
- 11159
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 66747
25.5%
2 33270
12.7%
4 25056
 
9.6%
6 25056
 
9.6%
1 25056
 
9.6%
5 25056
 
9.6%
3 16635
 
6.4%
7 16635
 
6.4%
. 13897
 
5.3%
- 11159
 
4.3%

holiday_lead_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2886470027295612
13042 
3.4644392304219376
 
855

Length

Max length19
Median length19
Mean length18.938476
Min length18

Characters and Unicode

Total characters263188
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.4644392304219376
2nd row3.4644392304219376
3rd row3.4644392304219376
4th row3.4644392304219376
5th row3.4644392304219376

Common Values

ValueCountFrequency (%)
-0.2886470027295612 13042
93.8%
3.4644392304219376 855
 
6.2%

Length

2024-07-14T18:21:03.734084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:03.870670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2886470027295612 13042
93.8%
3.4644392304219376 855
 
6.2%

Most occurring characters

ValueCountFrequency (%)
2 53878
20.5%
0 39981
15.2%
6 27794
10.6%
7 26939
10.2%
8 26084
9.9%
4 16462
 
6.3%
9 14752
 
5.6%
. 13897
 
5.3%
1 13897
 
5.3%
- 13042
 
5.0%
Other values (2) 16462
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 263188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 53878
20.5%
0 39981
15.2%
6 27794
10.6%
7 26939
10.2%
8 26084
9.9%
4 16462
 
6.3%
9 14752
 
5.6%
. 13897
 
5.3%
1 13897
 
5.3%
- 13042
 
5.0%
Other values (2) 16462
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 263188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 53878
20.5%
0 39981
15.2%
6 27794
10.6%
7 26939
10.2%
8 26084
9.9%
4 16462
 
6.3%
9 14752
 
5.6%
. 13897
 
5.3%
1 13897
 
5.3%
- 13042
 
5.0%
Other values (2) 16462
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 263188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 53878
20.5%
0 39981
15.2%
6 27794
10.6%
7 26939
10.2%
8 26084
9.9%
4 16462
 
6.3%
9 14752
 
5.6%
. 13897
 
5.3%
1 13897
 
5.3%
- 13042
 
5.0%
Other values (2) 16462
 
6.3%

workday_lead_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10004 
-1.591758015261153
3893 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10004
72.0%
-1.591758015261153 3893
 
28.0%

Length

2024-07-14T18:21:04.039248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:04.188820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10004
72.0%
1.591758015261153 3893
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43909
17.6%
1 39473
15.8%
5 25576
10.2%
6 23901
9.6%
2 23901
9.6%
9 23901
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43909
17.6%
1 39473
15.8%
5 25576
10.2%
6 23901
9.6%
2 23901
9.6%
9 23901
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43909
17.6%
1 39473
15.8%
5 25576
10.2%
6 23901
9.6%
2 23901
9.6%
9 23901
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43909
17.6%
1 39473
15.8%
5 25576
10.2%
6 23901
9.6%
2 23901
9.6%
9 23901
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lead_2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4307672385156441
11158 
2.3214393077937907
2739 

Length

Max length19
Median length19
Mean length18.802907
Min length18

Characters and Unicode

Total characters261304
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4307672385156441
2nd row-0.4307672385156441
3rd row-0.4307672385156441
4th row-0.4307672385156441
5th row-0.4307672385156441

Common Values

ValueCountFrequency (%)
-0.4307672385156441 11158
80.3%
2.3214393077937907 2739
 
19.7%

Length

2024-07-14T18:21:04.348393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:04.480041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4307672385156441 11158
80.3%
2.3214393077937907 2739
 
19.7%

Most occurring characters

ValueCountFrequency (%)
4 36213
13.9%
3 33272
12.7%
7 33272
12.7%
0 27794
10.6%
1 25055
9.6%
6 22316
8.5%
5 22316
8.5%
2 16636
6.4%
. 13897
 
5.3%
- 11158
 
4.3%
Other values (2) 19375
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 36213
13.9%
3 33272
12.7%
7 33272
12.7%
0 27794
10.6%
1 25055
9.6%
6 22316
8.5%
5 22316
8.5%
2 16636
6.4%
. 13897
 
5.3%
- 11158
 
4.3%
Other values (2) 19375
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 36213
13.9%
3 33272
12.7%
7 33272
12.7%
0 27794
10.6%
1 25055
9.6%
6 22316
8.5%
5 22316
8.5%
2 16636
6.4%
. 13897
 
5.3%
- 11158
 
4.3%
Other values (2) 19375
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 36213
13.9%
3 33272
12.7%
7 33272
12.7%
0 27794
10.6%
1 25055
9.6%
6 22316
8.5%
5 22316
8.5%
2 16636
6.4%
. 13897
 
5.3%
- 11158
 
4.3%
Other values (2) 19375
7.4%

holiday_lead_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2884640993048223
13043 
3.466635891294369
 
854

Length

Max length19
Median length19
Mean length18.877096
Min length17

Characters and Unicode

Total characters262335
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.466635891294369
2nd row3.466635891294369
3rd row3.466635891294369
4th row3.466635891294369
5th row3.466635891294369

Common Values

ValueCountFrequency (%)
-0.2884640993048223 13043
93.9%
3.466635891294369 854
 
6.1%

Length

2024-07-14T18:21:04.631636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:04.773290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2884640993048223 13043
93.9%
3.466635891294369 854
 
6.1%

Most occurring characters

ValueCountFrequency (%)
4 40837
15.6%
2 39983
15.2%
8 39983
15.2%
0 39129
14.9%
9 28648
10.9%
3 28648
10.9%
6 16459
6.3%
. 13897
 
5.3%
- 13043
 
5.0%
5 854
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 40837
15.6%
2 39983
15.2%
8 39983
15.2%
0 39129
14.9%
9 28648
10.9%
3 28648
10.9%
6 16459
6.3%
. 13897
 
5.3%
- 13043
 
5.0%
5 854
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 40837
15.6%
2 39983
15.2%
8 39983
15.2%
0 39129
14.9%
9 28648
10.9%
3 28648
10.9%
6 16459
6.3%
. 13897
 
5.3%
- 13043
 
5.0%
5 854
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 40837
15.6%
2 39983
15.2%
8 39983
15.2%
0 39129
14.9%
9 28648
10.9%
3 28648
10.9%
6 16459
6.3%
. 13897
 
5.3%
- 13043
 
5.0%
5 854
 
0.3%

workday_lead_2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10003 
-1.591758015261153
3894 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10003
72.0%
-1.591758015261153 3894
 
28.0%

Length

2024-07-14T18:21:04.910889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:05.048520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10003
72.0%
1.591758015261153 3894
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43906
17.6%
1 39476
15.8%
5 25579
10.2%
6 23900
9.6%
2 23900
9.6%
9 23900
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43906
17.6%
1 39476
15.8%
5 25579
10.2%
6 23900
9.6%
2 23900
9.6%
9 23900
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43906
17.6%
1 39476
15.8%
5 25579
10.2%
6 23900
9.6%
2 23900
9.6%
9 23900
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43906
17.6%
1 39476
15.8%
5 25579
10.2%
6 23900
9.6%
2 23900
9.6%
9 23900
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lead_3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4309139822148492
11157 
2.3206487634959365
2740 

Length

Max length19
Median length19
Mean length18.802835
Min length18

Characters and Unicode

Total characters261303
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4309139822148492
2nd row-0.4309139822148492
3rd row-0.4309139822148492
4th row-0.4309139822148492
5th row-0.4309139822148492

Common Values

ValueCountFrequency (%)
-0.4309139822148492 11157
80.3%
2.3206487634959365 2740
 
19.7%

Length

2024-07-14T18:21:05.214115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:05.368663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4309139822148492 11157
80.3%
2.3206487634959365 2740
 
19.7%

Most occurring characters

ValueCountFrequency (%)
4 38951
14.9%
9 38951
14.9%
2 38951
14.9%
3 30534
11.7%
0 25054
9.6%
8 25054
9.6%
1 22314
8.5%
. 13897
 
5.3%
- 11157
 
4.3%
6 8220
 
3.1%
Other values (2) 8220
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 38951
14.9%
9 38951
14.9%
2 38951
14.9%
3 30534
11.7%
0 25054
9.6%
8 25054
9.6%
1 22314
8.5%
. 13897
 
5.3%
- 11157
 
4.3%
6 8220
 
3.1%
Other values (2) 8220
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 38951
14.9%
9 38951
14.9%
2 38951
14.9%
3 30534
11.7%
0 25054
9.6%
8 25054
9.6%
1 22314
8.5%
. 13897
 
5.3%
- 11157
 
4.3%
6 8220
 
3.1%
Other values (2) 8220
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 38951
14.9%
9 38951
14.9%
2 38951
14.9%
3 30534
11.7%
0 25054
9.6%
8 25054
9.6%
1 22314
8.5%
. 13897
 
5.3%
- 11157
 
4.3%
6 8220
 
3.1%
Other values (2) 8220
 
3.1%

holiday_lead_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2882811155085369
13044 
3.46883630665841
 
853

Length

Max length19
Median length19
Mean length18.81586
Min length16

Characters and Unicode

Total characters261484
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.46883630665841
2nd row3.46883630665841
3rd row3.46883630665841
4th row3.46883630665841
5th row3.46883630665841

Common Values

ValueCountFrequency (%)
-0.2882811155085369 13044
93.9%
3.46883630665841 853
 
6.1%

Length

2024-07-14T18:21:05.549184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:05.699778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2882811155085369 13044
93.9%
3.46883630665841 853
 
6.1%

Most occurring characters

ValueCountFrequency (%)
8 54735
20.9%
1 39985
15.3%
5 39985
15.3%
0 26941
10.3%
2 26088
10.0%
6 16456
 
6.3%
3 15603
 
6.0%
. 13897
 
5.3%
- 13044
 
5.0%
9 13044
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 54735
20.9%
1 39985
15.3%
5 39985
15.3%
0 26941
10.3%
2 26088
10.0%
6 16456
 
6.3%
3 15603
 
6.0%
. 13897
 
5.3%
- 13044
 
5.0%
9 13044
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 54735
20.9%
1 39985
15.3%
5 39985
15.3%
0 26941
10.3%
2 26088
10.0%
6 16456
 
6.3%
3 15603
 
6.0%
. 13897
 
5.3%
- 13044
 
5.0%
9 13044
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 54735
20.9%
1 39985
15.3%
5 39985
15.3%
0 26941
10.3%
2 26088
10.0%
6 16456
 
6.3%
3 15603
 
6.0%
. 13897
 
5.3%
- 13044
 
5.0%
9 13044
 
5.0%

workday_lead_3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10002 
-1.591758015261153
3895 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10002
72.0%
-1.591758015261153 3895
 
28.0%

Length

2024-07-14T18:21:05.862345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:05.999975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10002
72.0%
1.591758015261153 3895
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43903
17.6%
1 39479
15.8%
5 25582
10.2%
6 23899
9.6%
2 23899
9.6%
9 23899
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43903
17.6%
1 39479
15.8%
5 25582
10.2%
6 23899
9.6%
2 23899
9.6%
9 23899
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43903
17.6%
1 39479
15.8%
5 25582
10.2%
6 23899
9.6%
2 23899
9.6%
9 23899
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43903
17.6%
1 39479
15.8%
5 25582
10.2%
6 23899
9.6%
2 23899
9.6%
9 23899
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lead_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.4310607072482172
11156 
2.3198588578943955
2741 

Length

Max length19
Median length19
Mean length18.802763
Min length18

Characters and Unicode

Total characters261302
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4310607072482172
2nd row-0.4310607072482172
3rd row-0.4310607072482172
4th row-0.4310607072482172
5th row-0.4310607072482172

Common Values

ValueCountFrequency (%)
-0.4310607072482172 11156
80.3%
2.3198588578943955 2741
 
19.7%

Length

2024-07-14T18:21:06.146583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:06.269255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4310607072482172 11156
80.3%
2.3198588578943955 2741
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 44624
17.1%
7 36209
13.9%
2 36209
13.9%
4 25053
9.6%
1 25053
9.6%
8 22120
8.5%
3 16638
 
6.4%
. 13897
 
5.3%
- 11156
 
4.3%
6 11156
 
4.3%
Other values (2) 19187
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 44624
17.1%
7 36209
13.9%
2 36209
13.9%
4 25053
9.6%
1 25053
9.6%
8 22120
8.5%
3 16638
 
6.4%
. 13897
 
5.3%
- 11156
 
4.3%
6 11156
 
4.3%
Other values (2) 19187
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 44624
17.1%
7 36209
13.9%
2 36209
13.9%
4 25053
9.6%
1 25053
9.6%
8 22120
8.5%
3 16638
 
6.4%
. 13897
 
5.3%
- 11156
 
4.3%
6 11156
 
4.3%
Other values (2) 19187
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 44624
17.1%
7 36209
13.9%
2 36209
13.9%
4 25053
9.6%
1 25053
9.6%
8 22120
8.5%
3 16638
 
6.4%
. 13897
 
5.3%
- 11156
 
4.3%
6 11156
 
4.3%
Other values (2) 19187
7.3%

holiday_lead_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2880980511771303
13045 
3.471040487480332
 
852

Length

Max length19
Median length19
Mean length18.877384
Min length17

Characters and Unicode

Total characters262339
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.471040487480332
2nd row3.471040487480332
3rd row3.471040487480332
4th row3.471040487480332
5th row3.471040487480332

Common Values

ValueCountFrequency (%)
-0.2880980511771303 13045
93.9%
3.471040487480332 852
 
6.1%

Length

2024-07-14T18:21:06.417857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:06.551502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2880980511771303 13045
93.9%
3.471040487480332 852
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 54736
20.9%
8 40839
15.6%
1 39987
15.2%
3 28646
10.9%
7 27794
10.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13045
 
5.0%
9 13045
 
5.0%
5 13045
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 54736
20.9%
8 40839
15.6%
1 39987
15.2%
3 28646
10.9%
7 27794
10.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13045
 
5.0%
9 13045
 
5.0%
5 13045
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 54736
20.9%
8 40839
15.6%
1 39987
15.2%
3 28646
10.9%
7 27794
10.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13045
 
5.0%
9 13045
 
5.0%
5 13045
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 54736
20.9%
8 40839
15.6%
1 39987
15.2%
3 28646
10.9%
7 27794
10.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13045
 
5.0%
9 13045
 
5.0%
5 13045
 
5.0%

workday_lead_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10001 
-1.591758015261153
3896 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10001
72.0%
-1.591758015261153 3896
 
28.0%

Length

2024-07-14T18:21:06.726034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:06.869651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10001
72.0%
1.591758015261153 3896
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43900
17.5%
1 39482
15.8%
5 25585
10.2%
6 23898
9.6%
2 23898
9.6%
9 23898
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43900
17.5%
1 39482
15.8%
5 25585
10.2%
6 23898
9.6%
2 23898
9.6%
9 23898
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43900
17.5%
1 39482
15.8%
5 25585
10.2%
6 23898
9.6%
2 23898
9.6%
9 23898
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43900
17.5%
1 39482
15.8%
5 25585
10.2%
6 23898
9.6%
2 23898
9.6%
9 23898
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

event_lead_5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.431207413644813
11155 
2.3190695900783003
2742 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.431207413644813
2nd row-0.431207413644813
3rd row-0.431207413644813
4th row-0.431207413644813
5th row-0.431207413644813

Common Values

ValueCountFrequency (%)
-0.431207413644813 11155
80.3%
2.3190695900783003 2742
 
19.7%

Length

2024-07-14T18:21:07.034209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:07.173837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.431207413644813 11155
80.3%
2.3190695900783003 2742
 
19.7%

Most occurring characters

ValueCountFrequency (%)
4 44620
17.8%
3 41691
16.7%
1 36207
14.5%
0 36020
14.4%
. 13897
 
5.6%
2 13897
 
5.6%
7 13897
 
5.6%
6 13897
 
5.6%
8 13897
 
5.6%
- 11155
 
4.5%
Other values (2) 10968
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 44620
17.8%
3 41691
16.7%
1 36207
14.5%
0 36020
14.4%
. 13897
 
5.6%
2 13897
 
5.6%
7 13897
 
5.6%
6 13897
 
5.6%
8 13897
 
5.6%
- 11155
 
4.5%
Other values (2) 10968
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 44620
17.8%
3 41691
16.7%
1 36207
14.5%
0 36020
14.4%
. 13897
 
5.6%
2 13897
 
5.6%
7 13897
 
5.6%
6 13897
 
5.6%
8 13897
 
5.6%
- 11155
 
4.5%
Other values (2) 10968
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 44620
17.8%
3 41691
16.7%
1 36207
14.5%
0 36020
14.4%
. 13897
 
5.6%
2 13897
 
5.6%
7 13897
 
5.6%
6 13897
 
5.6%
8 13897
 
5.6%
- 11155
 
4.5%
Other values (2) 10968
 
4.4%

holiday_lead_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
-0.2879149061465477
13046 
3.473248444771398
 
851

Length

Max length19
Median length19
Mean length18.877528
Min length17

Characters and Unicode

Total characters262341
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.473248444771398
2nd row3.473248444771398
3rd row3.473248444771398
4th row3.473248444771398
5th row3.473248444771398

Common Values

ValueCountFrequency (%)
-0.2879149061465477 13046
93.9%
3.473248444771398 851
 
6.1%

Length

2024-07-14T18:21:07.356348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:07.527910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2879149061465477 13046
93.9%
3.473248444771398 851
 
6.1%

Most occurring characters

ValueCountFrequency (%)
4 43393
16.5%
7 41691
15.9%
9 26943
10.3%
1 26943
10.3%
0 26092
9.9%
6 26092
9.9%
8 14748
 
5.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13046
 
5.0%
Other values (2) 15599
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 43393
16.5%
7 41691
15.9%
9 26943
10.3%
1 26943
10.3%
0 26092
9.9%
6 26092
9.9%
8 14748
 
5.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13046
 
5.0%
Other values (2) 15599
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 43393
16.5%
7 41691
15.9%
9 26943
10.3%
1 26943
10.3%
0 26092
9.9%
6 26092
9.9%
8 14748
 
5.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13046
 
5.0%
Other values (2) 15599
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 43393
16.5%
7 41691
15.9%
9 26943
10.3%
1 26943
10.3%
0 26092
9.9%
6 26092
9.9%
8 14748
 
5.6%
. 13897
 
5.3%
2 13897
 
5.3%
- 13046
 
5.0%
Other values (2) 15599
 
5.9%

workday_lead_5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.6282361957109004
10000 
-1.591758015261153
3897 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters250146
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6282361957109004
2nd row0.6282361957109004
3rd row0.6282361957109004
4th row0.6282361957109004
5th row0.6282361957109004

Common Values

ValueCountFrequency (%)
0.6282361957109004 10000
72.0%
-1.591758015261153 3897
 
28.0%

Length

2024-07-14T18:21:07.681521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:07.829083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6282361957109004 10000
72.0%
1.591758015261153 3897
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 43897
17.5%
1 39485
15.8%
5 25588
10.2%
6 23897
9.6%
2 23897
9.6%
9 23897
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43897
17.5%
1 39485
15.8%
5 25588
10.2%
6 23897
9.6%
2 23897
9.6%
9 23897
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43897
17.5%
1 39485
15.8%
5 25588
10.2%
6 23897
9.6%
2 23897
9.6%
9 23897
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43897
17.5%
1 39485
15.8%
5 25588
10.2%
6 23897
9.6%
2 23897
9.6%
9 23897
9.6%
. 13897
 
5.6%
8 13897
 
5.6%
3 13897
 
5.6%
7 13897
 
5.6%
Other values (2) 13897
 
5.6%

temp_lag_1
Real number (ℝ)

HIGH CORRELATION 

Distinct377
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29788182
Minimum-3.1213596
Maximum3.5619001
Zeros0
Zeros (%)0.0%
Negative5880
Negative (%)42.3%
Memory size108.7 KiB
2024-07-14T18:21:07.998629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.1213596
5-th percentile-1.6258049
Q1-0.41066682
median0.22805964
Q31.0693091
95-th percentile2.1598177
Maximum3.5619001
Range6.6832597
Interquartile range (IQR)1.4799759

Descriptive statistics

Standard deviation1.1218523
Coefficient of variation (CV)3.7660984
Kurtosis-0.13742939
Mean0.29788182
Median Absolute Deviation (MAD)0.73219862
Skewness-0.02724134
Sum4139.6637
Variance1.2585525
MonotonicityNot monotonic
2024-07-14T18:21:08.176155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1146716313 156
 
1.1%
-0.0679355495 153
 
1.1%
-0.1925651011 138
 
1.0%
-0.1769864071 135
 
1.0%
-0.2237224889 132
 
0.9%
-0.3016159587 129
 
0.9%
-0.02119946766 129
 
0.9%
-0.2548798768 126
 
0.9%
-0.1458290192 123
 
0.9%
0.05669400206 117
 
0.8%
Other values (367) 12559
90.4%
ValueCountFrequency (%)
-3.121359563 6
< 0.1%
-3.090202175 9
0.1%
-3.074623481 3
 
< 0.1%
-3.043466093 3
 
< 0.1%
-2.934415235 3
 
< 0.1%
-2.887679154 3
 
< 0.1%
-2.809785684 6
< 0.1%
-2.763049602 3
 
< 0.1%
-2.700734826 3
 
< 0.1%
-2.63842005 3
 
< 0.1%
ValueCountFrequency (%)
3.56190014 3
 
< 0.1%
3.515164058 3
 
< 0.1%
3.437270588 12
0.1%
3.4061132 6
< 0.1%
3.390534506 3
 
< 0.1%
3.359377118 3
 
< 0.1%
3.265904955 3
 
< 0.1%
3.250326261 6
< 0.1%
3.234747567 6
< 0.1%
3.188011485 6
< 0.1%

humidity_lag_1
Real number (ℝ)

HIGH CORRELATION 

Distinct3067
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.074069433
Minimum-3.2931096
Maximum1.6010646
Zeros0
Zeros (%)0.0%
Negative6315
Negative (%)45.4%
Memory size108.7 KiB
2024-07-14T18:21:08.361660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.2931096
5-th percentile-2.0882141
Q1-0.75967054
median0.13812113
Q30.7644249
95-th percentile1.2758167
Maximum1.6010646
Range4.8941742
Interquartile range (IQR)1.5240954

Descriptive statistics

Standard deviation1.042829
Coefficient of variation (CV)-14.079074
Kurtosis-0.43763291
Mean-0.074069433
Median Absolute Deviation (MAD)0.71635174
Skewness-0.65780749
Sum-1029.3429
Variance1.0874924
MonotonicityNot monotonic
2024-07-14T18:21:08.554143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7153689135 18
 
0.1%
0.7489689012 18
 
0.1%
0.7200729118 18
 
0.1%
1.027176799 18
 
0.1%
0.9808088159 16
 
0.1%
0.8107928785 15
 
0.1%
1.26909671 15
 
0.1%
1.384008668 15
 
0.1%
0.7825688888 15
 
0.1%
0.9606488234 15
 
0.1%
Other values (3057) 13734
98.8%
ValueCountFrequency (%)
-3.293109613 3
< 0.1%
-3.201717646 3
< 0.1%
-3.113013679 3
< 0.1%
-3.103605682 3
< 0.1%
-3.045141704 3
< 0.1%
-2.952405738 3
< 0.1%
-2.94770174 3
< 0.1%
-2.938293743 3
< 0.1%
-2.928213747 3
< 0.1%
-2.926197747 3
< 0.1%
ValueCountFrequency (%)
1.601064588 3
 
< 0.1%
1.600392588 3
 
< 0.1%
1.599720588 3
 
< 0.1%
1.598376589 9
0.1%
1.597704589 3
 
< 0.1%
1.59636059 3
 
< 0.1%
1.59568859 3
 
< 0.1%
1.593672591 3
 
< 0.1%
1.593000591 3
 
< 0.1%
1.592328591 6
< 0.1%

precip_lag_1
Real number (ℝ)

HIGH CORRELATION 

Distinct404
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010804476
Minimum-0.19875046
Maximum35.186123
Zeros0
Zeros (%)0.0%
Negative12284
Negative (%)88.4%
Memory size108.7 KiB
2024-07-14T18:21:08.755652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.19875046
5-th percentile-0.19875046
Q1-0.19875046
median-0.19875046
Q3-0.19875046
95-th percentile0.88950753
Maximum35.186123
Range35.384873
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1736181
Coefficient of variation (CV)108.62332
Kurtosis297.56151
Mean0.010804476
Median Absolute Deviation (MAD)0
Skewness14.117775
Sum150.14981
Variance1.3773794
MonotonicityNot monotonic
2024-07-14T18:21:08.956071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1943534597 300
 
2.2%
-0.1965519606 213
 
1.5%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1855594557 69
 
0.5%
-0.1877579567 66
 
0.5%
-0.1833609547 54
 
0.4%
0.01010713218 54
 
0.4%
-0.1789639527 36
 
0.3%
Other values (394) 1949
 
14.0%
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1965519606 213
 
1.5%
-0.1943534597 300
 
2.2%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1877579567 66
 
0.5%
-0.1855594557 69
 
0.5%
-0.1833609547 54
 
0.4%
-0.1811624537 36
 
0.3%
-0.1789639527 36
 
0.3%
ValueCountFrequency (%)
35.18612293 3
< 0.1%
26.04695433 3
< 0.1%
22.98004545 3
< 0.1%
16.31858746 3
< 0.1%
15.46556907 3
< 0.1%
15.01267787 3
< 0.1%
13.9683899 3
< 0.1%
13.94420639 3
< 0.1%
12.39206469 3
< 0.1%
11.31919621 3
< 0.1%

temp_lag_2
Real number (ℝ)

HIGH CORRELATION 

Distinct377
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29781453
Minimum-3.1212394
Maximum3.561919
Zeros0
Zeros (%)0.0%
Negative5881
Negative (%)42.3%
Memory size108.7 KiB
2024-07-14T18:21:09.161520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.1212394
5-th percentile-1.6257074
Q1-0.41058772
median0.22812905
Q31.0693658
95-th percentile2.1598578
Maximum3.561919
Range6.6831584
Interquartile range (IQR)1.4799535

Descriptive statistics

Standard deviation1.1218314
Coefficient of variation (CV)3.7668794
Kurtosis-0.13741321
Mean0.29781453
Median Absolute Deviation (MAD)0.73218751
Skewness-0.026964482
Sum4138.7286
Variance1.2585058
MonotonicityNot monotonic
2024-07-14T18:21:09.352011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1145970238 156
 
1.1%
-0.06786165074 153
 
1.1%
-0.1924893124 138
 
1.0%
-0.1769108547 135
 
1.0%
-0.2236462278 132
 
0.9%
-0.02112627762 129
 
0.9%
-0.3015385163 129
 
0.9%
-0.2548031432 126
 
0.9%
-0.1457539393 123
 
0.9%
0.1969721302 117
 
0.8%
Other values (367) 12559
90.4%
ValueCountFrequency (%)
-3.121239361 6
< 0.1%
-3.090082445 9
0.1%
-3.074503988 3
 
< 0.1%
-3.043347072 3
 
< 0.1%
-2.934297868 3
 
< 0.1%
-2.887562495 3
 
< 0.1%
-2.809670207 6
< 0.1%
-2.762934834 3
 
< 0.1%
-2.700621003 3
 
< 0.1%
-2.638307172 3
 
< 0.1%
ValueCountFrequency (%)
3.561918994 3
 
< 0.1%
3.515183621 3
 
< 0.1%
3.437291333 12
0.1%
3.406134417 6
< 0.1%
3.39055596 3
 
< 0.1%
3.359399044 3
 
< 0.1%
3.265928298 3
 
< 0.1%
3.25034984 6
< 0.1%
3.234771383 6
< 0.1%
3.188036009 6
< 0.1%

humidity_lag_2
Real number (ℝ)

HIGH CORRELATION 

Distinct3067
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.073953141
Minimum-3.2931684
Maximum1.6011766
Zeros0
Zeros (%)0.0%
Negative6314
Negative (%)45.4%
Memory size108.7 KiB
2024-07-14T18:21:09.543497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.2931684
5-th percentile-2.0882308
Q1-0.75964091
median0.13818211
Q30.76450774
95-th percentile1.2759174
Maximum1.6011766
Range4.8943451
Interquartile range (IQR)1.5241486

Descriptive statistics

Standard deviation1.0428623
Coefficient of variation (CV)-14.101663
Kurtosis-0.43744582
Mean-0.073953141
Median Absolute Deviation (MAD)0.71637675
Skewness-0.65798293
Sum-1027.7268
Variance1.0875618
MonotonicityNot monotonic
2024-07-14T18:21:09.737979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7154500462 18
 
0.1%
0.7490512069 18
 
0.1%
0.7201542087 18
 
0.1%
1.027268817 18
 
0.1%
0.9808992155 16
 
0.1%
0.8108773425 15
 
0.1%
1.269197174 15
 
0.1%
1.384113144 15
 
0.1%
0.7826523676 15
 
0.1%
0.9607385191 15
 
0.1%
Other values (3057) 13734
98.8%
ValueCountFrequency (%)
-3.293168422 3
< 0.1%
-3.201773265 3
< 0.1%
-3.113066201 3
< 0.1%
-3.103657876 3
< 0.1%
-3.045191856 3
< 0.1%
-2.952452653 3
< 0.1%
-2.94774849 3
< 0.1%
-2.938340165 3
< 0.1%
-2.928259817 3
< 0.1%
-2.926243747 3
< 0.1%
ValueCountFrequency (%)
1.601176641 3
 
< 0.1%
1.600504618 3
 
< 0.1%
1.599832595 3
 
< 0.1%
1.598488549 9
0.1%
1.597816525 3
 
< 0.1%
1.596472479 3
 
< 0.1%
1.595800456 3
 
< 0.1%
1.593784386 3
 
< 0.1%
1.593112363 3
 
< 0.1%
1.59244034 6
< 0.1%

precip_lag_2
Real number (ℝ)

HIGH CORRELATION 

Distinct404
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010804476
Minimum-0.19875046
Maximum35.186123
Zeros0
Zeros (%)0.0%
Negative12284
Negative (%)88.4%
Memory size108.7 KiB
2024-07-14T18:21:09.923482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.19875046
5-th percentile-0.19875046
Q1-0.19875046
median-0.19875046
Q3-0.19875046
95-th percentile0.88950753
Maximum35.186123
Range35.384873
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1736181
Coefficient of variation (CV)108.62332
Kurtosis297.56151
Mean0.010804476
Median Absolute Deviation (MAD)0
Skewness14.117775
Sum150.14981
Variance1.3773794
MonotonicityNot monotonic
2024-07-14T18:21:10.132923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1943534597 300
 
2.2%
-0.1965519606 213
 
1.5%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1855594557 69
 
0.5%
-0.1877579567 66
 
0.5%
-0.1833609547 54
 
0.4%
0.01010713218 54
 
0.4%
-0.1789639527 36
 
0.3%
Other values (394) 1949
 
14.0%
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1965519606 213
 
1.5%
-0.1943534597 300
 
2.2%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1877579567 66
 
0.5%
-0.1855594557 69
 
0.5%
-0.1833609547 54
 
0.4%
-0.1811624537 36
 
0.3%
-0.1789639527 36
 
0.3%
ValueCountFrequency (%)
35.18612293 3
< 0.1%
26.04695433 3
< 0.1%
22.98004545 3
< 0.1%
16.31858746 3
< 0.1%
15.46556907 3
< 0.1%
15.01267787 3
< 0.1%
13.9683899 3
< 0.1%
13.94420639 3
< 0.1%
12.39206469 3
< 0.1%
11.31919621 3
< 0.1%

temp_lag_3
Real number (ℝ)

HIGH CORRELATION 

Distinct377
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29774727
Minimum-3.1211151
Maximum3.5619362
Zeros0
Zeros (%)0.0%
Negative5882
Negative (%)42.3%
Memory size108.7 KiB
2024-07-14T18:21:10.329395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.1211151
5-th percentile-1.6256071
Q1-0.41050687
median0.22819966
Q31.0694229
95-th percentile2.1598975
Maximum3.5619362
Range6.6830512
Interquartile range (IQR)1.4799298

Descriptive statistics

Standard deviation1.1218106
Coefficient of variation (CV)3.7676602
Kurtosis-0.13740406
Mean0.29774727
Median Absolute Deviation (MAD)0.73217578
Skewness-0.026686571
Sum4137.7938
Variance1.2584589
MonotonicityNot monotonic
2024-07-14T18:21:10.508916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1145209183 156
 
1.1%
-0.06778629432 153
 
1.1%
-0.1924119584 138
 
1.0%
-0.1768337504 135
 
1.0%
-0.2235683744 132
 
0.9%
-0.3014594144 129
 
0.9%
-0.02105167031 129
 
0.9%
-0.2547247904 126
 
0.9%
-0.1456773343 123
 
0.9%
0.05683936971 117
 
0.8%
Other values (367) 12559
90.4%
ValueCountFrequency (%)
-3.121115063 6
< 0.1%
-3.089958647 9
0.1%
-3.074380439 3
 
< 0.1%
-3.043224023 3
 
< 0.1%
-2.934176567 3
 
< 0.1%
-2.887441943 3
 
< 0.1%
-2.809550903 6
< 0.1%
-2.762816279 3
 
< 0.1%
-2.700503447 3
 
< 0.1%
-2.638190615 3
 
< 0.1%
ValueCountFrequency (%)
3.561936171 3
 
< 0.1%
3.515201547 3
 
< 0.1%
3.437310507 12
0.1%
3.406154091 6
< 0.1%
3.390575883 3
 
< 0.1%
3.359419467 3
 
< 0.1%
3.265950219 3
 
< 0.1%
3.250372011 6
< 0.1%
3.234793803 6
< 0.1%
3.188059179 6
< 0.1%

humidity_lag_3
Real number (ℝ)

HIGH CORRELATION 

Distinct3067
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.073843405
Minimum-3.2931958
Maximum1.6012126
Zeros0
Zeros (%)0.0%
Negative6313
Negative (%)45.4%
Memory size108.7 KiB
2024-07-14T18:21:10.716361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.2931958
5-th percentile-2.0882426
Q1-0.7596355
median0.13819913
Q30.76453286
95-th percentile1.2759491
Maximum1.6012126
Range4.8944084
Interquartile range (IQR)1.5241684

Descriptive statistics

Standard deviation1.0428918
Coefficient of variation (CV)-14.123019
Kurtosis-0.4373358
Mean-0.073843405
Median Absolute Deviation (MAD)0.71638601
Skewness-0.65818244
Sum-1026.2018
Variance1.0876234
MonotonicityNot monotonic
2024-07-14T18:21:10.929826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7154745351 18
 
0.1%
1.02729734 18
 
0.1%
0.7490761305 18
 
0.1%
0.7201787585 18
 
0.1%
0.9809271383 16
 
0.1%
0.1207262975 15
 
0.1%
0.75243629 15
 
0.1%
0.9338849049 15
 
0.1%
0.8109030659 15
 
0.1%
0.7517642581 15
 
0.1%
Other values (3057) 13734
98.8%
ValueCountFrequency (%)
-3.29319579 3
< 0.1%
-3.20179945 3
< 0.1%
-3.113091239 3
< 0.1%
-3.103682792 3
< 0.1%
-3.045216016 3
< 0.1%
-2.952475613 3
< 0.1%
-2.947771389 3
< 0.1%
-2.938362943 3
< 0.1%
-2.928282464 3
< 0.1%
-2.926266368 3
< 0.1%
ValueCountFrequency (%)
1.601212588 3
 
< 0.1%
1.600540557 3
 
< 0.1%
1.599868525 3
 
< 0.1%
1.598524461 9
0.1%
1.597852429 3
 
< 0.1%
1.596508365 3
 
< 0.1%
1.595836333 3
 
< 0.1%
1.593820237 3
 
< 0.1%
1.593148206 3
 
< 0.1%
1.592476174 6
< 0.1%

precip_lag_3
Real number (ℝ)

HIGH CORRELATION 

Distinct404
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010804476
Minimum-0.19875046
Maximum35.186123
Zeros0
Zeros (%)0.0%
Negative12284
Negative (%)88.4%
Memory size108.7 KiB
2024-07-14T18:21:11.114298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.19875046
5-th percentile-0.19875046
Q1-0.19875046
median-0.19875046
Q3-0.19875046
95-th percentile0.88950753
Maximum35.186123
Range35.384873
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1736181
Coefficient of variation (CV)108.62332
Kurtosis297.56151
Mean0.010804476
Median Absolute Deviation (MAD)0
Skewness14.117775
Sum150.14981
Variance1.3773794
MonotonicityNot monotonic
2024-07-14T18:21:11.319749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1943534597 300
 
2.2%
-0.1965519606 213
 
1.5%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1855594557 69
 
0.5%
-0.1877579567 66
 
0.5%
-0.1833609547 54
 
0.4%
0.01010713218 54
 
0.4%
-0.1789639527 36
 
0.3%
Other values (394) 1949
 
14.0%
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1965519606 213
 
1.5%
-0.1943534597 300
 
2.2%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1877579567 66
 
0.5%
-0.1855594557 69
 
0.5%
-0.1833609547 54
 
0.4%
-0.1811624537 36
 
0.3%
-0.1789639527 36
 
0.3%
ValueCountFrequency (%)
35.18612293 3
< 0.1%
26.04695433 3
< 0.1%
22.98004545 3
< 0.1%
16.31858746 3
< 0.1%
15.46556907 3
< 0.1%
15.01267787 3
< 0.1%
13.9683899 3
< 0.1%
13.94420639 3
< 0.1%
12.39206469 3
< 0.1%
11.31919621 3
< 0.1%

temp_lag_4
Real number (ℝ)

HIGH CORRELATION 

Distinct377
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29768001
Minimum-3.1209908
Maximum3.5619534
Zeros0
Zeros (%)0.0%
Negative5883
Negative (%)42.3%
Memory size108.7 KiB
2024-07-14T18:21:11.534173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.1209908
5-th percentile-1.6255068
Q1-0.41042603
median0.22827027
Q31.06948
95-th percentile2.1599371
Maximum3.5619534
Range6.6829442
Interquartile range (IQR)1.479906

Descriptive statistics

Standard deviation1.1217897
Coefficient of variation (CV)3.7684413
Kurtosis-0.13739464
Mean0.29768001
Median Absolute Deviation (MAD)0.73216404
Skewness-0.026408661
Sum4136.8591
Variance1.258412
MonotonicityNot monotonic
2024-07-14T18:21:11.723668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1144448159 156
 
1.1%
-0.06771094072 153
 
1.1%
-0.1923346079 138
 
1.0%
-0.1767566495 135
 
1.0%
-0.2234905247 132
 
0.9%
-0.3013803168 129
 
0.9%
-0.02097706551 129
 
0.9%
-0.2546464415 126
 
0.9%
-0.1456007327 123
 
0.9%
0.0569127265 117
 
0.8%
Other values (367) 12559
90.4%
ValueCountFrequency (%)
-3.120990788 6
< 0.1%
-3.089834871 9
0.1%
-3.074256912 3
 
< 0.1%
-3.043100996 3
 
< 0.1%
-2.934055287 3
 
< 0.1%
-2.887321412 3
 
< 0.1%
-2.80943162 6
< 0.1%
-2.762697744 3
 
< 0.1%
-2.700385911 3
 
< 0.1%
-2.638074077 3
 
< 0.1%
ValueCountFrequency (%)
3.561953367 3
 
< 0.1%
3.515219492 3
 
< 0.1%
3.4373297 12
0.1%
3.406173783 6
< 0.1%
3.390595825 3
 
< 0.1%
3.359439908 3
 
< 0.1%
3.265972157 3
 
< 0.1%
3.250394199 6
< 0.1%
3.234816241 6
< 0.1%
3.188082365 6
< 0.1%

humidity_lag_4
Real number (ℝ)

HIGH CORRELATION 

Distinct3066
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.073762127
Minimum-3.2931772
Maximum1.6011814
Zeros0
Zeros (%)0.0%
Negative6312
Negative (%)45.4%
Memory size108.7 KiB
2024-07-14T18:21:11.938094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.2931772
5-th percentile-2.0882363
Q1-0.7596427
median0.13952684
Q30.76451015
95-th percentile1.2759212
Maximum1.6011814
Range4.8943586
Interquartile range (IQR)1.5241529

Descriptive statistics

Standard deviation1.0428972
Coefficient of variation (CV)-14.138655
Kurtosis-0.43722564
Mean-0.073762127
Median Absolute Deviation (MAD)0.7157067
Skewness-0.65838197
Sum-1025.0723
Variance1.0876346
MonotonicityNot monotonic
2024-07-14T18:21:12.137604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7154523232 18
 
0.1%
1.027271955 18
 
0.1%
0.7490535767 18
 
0.1%
0.7201564987 18
 
0.1%
0.9809022252 16
 
0.1%
0.1207101378 15
 
0.1%
0.752413702 15
 
0.1%
0.9338604704 15
 
0.1%
0.8108798829 15
 
0.1%
0.7517416769 15
 
0.1%
Other values (3056) 13734
98.8%
ValueCountFrequency (%)
-3.293177209 3
< 0.1%
-3.2017818 3
< 0.1%
-3.113074491 3
< 0.1%
-3.10366614 3
< 0.1%
-3.045199959 3
< 0.1%
-2.9524605 3
< 0.1%
-2.947756324 3
< 0.1%
-2.938347973 3
< 0.1%
-2.928267597 3
< 0.1%
-2.926251522 3
< 0.1%
ValueCountFrequency (%)
1.601181363 3
 
< 0.1%
1.600509338 3
 
< 0.1%
1.599837313 3
 
< 0.1%
1.598493263 9
0.1%
1.597821238 3
 
< 0.1%
1.596477188 3
 
< 0.1%
1.595805163 3
 
< 0.1%
1.593789088 3
 
< 0.1%
1.593117062 3
 
< 0.1%
1.592445037 6
< 0.1%

precip_lag_4
Real number (ℝ)

HIGH CORRELATION 

Distinct404
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010804476
Minimum-0.19875046
Maximum35.186123
Zeros0
Zeros (%)0.0%
Negative12284
Negative (%)88.4%
Memory size108.7 KiB
2024-07-14T18:21:12.315087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.19875046
5-th percentile-0.19875046
Q1-0.19875046
median-0.19875046
Q3-0.19875046
95-th percentile0.88950753
Maximum35.186123
Range35.384873
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1736181
Coefficient of variation (CV)108.62332
Kurtosis297.56151
Mean0.010804476
Median Absolute Deviation (MAD)0
Skewness14.117775
Sum150.14981
Variance1.3773794
MonotonicityNot monotonic
2024-07-14T18:21:12.492652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1943534597 300
 
2.2%
-0.1965519606 213
 
1.5%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1855594557 69
 
0.5%
-0.1877579567 66
 
0.5%
-0.1833609547 54
 
0.4%
0.01010713218 54
 
0.4%
-0.1789639527 36
 
0.3%
Other values (394) 1949
 
14.0%
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1965519606 213
 
1.5%
-0.1943534597 300
 
2.2%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1877579567 66
 
0.5%
-0.1855594557 69
 
0.5%
-0.1833609547 54
 
0.4%
-0.1811624537 36
 
0.3%
-0.1789639527 36
 
0.3%
ValueCountFrequency (%)
35.18612293 3
< 0.1%
26.04695433 3
< 0.1%
22.98004545 3
< 0.1%
16.31858746 3
< 0.1%
15.46556907 3
< 0.1%
15.01267787 3
< 0.1%
13.9683899 3
< 0.1%
13.94420639 3
< 0.1%
12.39206469 3
< 0.1%
11.31919621 3
< 0.1%

temp_lag_5
Real number (ℝ)

HIGH CORRELATION 

Distinct377
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29761275
Minimum-3.1208665
Maximum3.5619706
Zeros0
Zeros (%)0.0%
Negative5884
Negative (%)42.3%
Memory size108.7 KiB
2024-07-14T18:21:12.667142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.1208665
5-th percentile-1.6254065
Q1-0.41034519
median0.22834088
Q31.0695372
95-th percentile2.1599768
Maximum3.5619706
Range6.6828371
Interquartile range (IQR)1.4798823

Descriptive statistics

Standard deviation1.1217687
Coefficient of variation (CV)3.7692227
Kurtosis-0.13738495
Mean0.29761275
Median Absolute Deviation (MAD)0.73215232
Skewness-0.026130752
Sum4135.9245
Variance1.2583651
MonotonicityNot monotonic
2024-07-14T18:21:12.832746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1143687166 156
 
1.1%
-0.06763558992 153
 
1.1%
-0.1922572611 138
 
1.0%
-0.1766795522 135
 
1.0%
-0.2234126789 132
 
0.9%
-0.3013012234 129
 
0.9%
-0.02090246322 129
 
0.9%
-0.2545680967 126
 
0.9%
-0.1455241344 123
 
0.9%
0.05698608127 117
 
0.8%
Other values (367) 12559
90.4%
ValueCountFrequency (%)
-3.120866534 6
< 0.1%
-3.089711116 9
0.1%
-3.074133407 3
 
< 0.1%
-3.04297799 3
 
< 0.1%
-2.933934027 3
 
< 0.1%
-2.887200901 3
 
< 0.1%
-2.809312356 6
< 0.1%
-2.762579229 3
 
< 0.1%
-2.700268394 3
 
< 0.1%
-2.637957558 3
 
< 0.1%
ValueCountFrequency (%)
3.561970583 3
 
< 0.1%
3.515237457 3
 
< 0.1%
3.437348912 12
0.1%
3.406193494 6
< 0.1%
3.390615786 3
 
< 0.1%
3.359460368 3
 
< 0.1%
3.265994114 3
 
< 0.1%
3.250416405 6
< 0.1%
3.234838697 6
< 0.1%
3.18810557 6
< 0.1%

humidity_lag_5
Real number (ℝ)

HIGH CORRELATION 

Distinct3066
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.07370324
Minimum-3.2931586
Maximum1.6011501
Zeros0
Zeros (%)0.0%
Negative6311
Negative (%)45.4%
Memory size108.7 KiB
2024-07-14T18:21:12.998258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.2931586
5-th percentile-2.0882299
Q1-0.7596499
median0.13951049
Q30.76448744
95-th percentile1.2758933
Maximum1.6011501
Range4.8943088
Interquartile range (IQR)1.5241373

Descriptive statistics

Standard deviation1.0429098
Coefficient of variation (CV)-14.150121
Kurtosis-0.43723876
Mean-0.07370324
Median Absolute Deviation (MAD)0.71569942
Skewness-0.65851059
Sum-1024.2539
Variance1.0876608
MonotonicityNot monotonic
2024-07-14T18:21:13.165809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.027246571 18
 
0.1%
0.7490310236 18
 
0.1%
0.715430112 18
 
0.1%
0.7201342397 18
 
0.1%
0.9808773129 16
 
0.1%
0.8101846825 15
 
0.1%
0.9506364926 15
 
0.1%
0.8545378857 15
 
0.1%
1.269173134 15
 
0.1%
0.1206939784 15
 
0.1%
Other values (3056) 13734
98.8%
ValueCountFrequency (%)
-3.29315863 3
< 0.1%
-3.201764151 3
< 0.1%
-3.113057745 3
< 0.1%
-3.103649489 3
< 0.1%
-3.045183903 3
< 0.1%
-2.952445388 3
< 0.1%
-2.94774126 3
< 0.1%
-2.938333005 3
< 0.1%
-2.928252731 3
< 0.1%
-2.926236677 3
< 0.1%
ValueCountFrequency (%)
1.601150139 3
 
< 0.1%
1.600478121 3
 
< 0.1%
1.599806103 3
 
< 0.1%
1.598462066 9
0.1%
1.597790048 3
 
< 0.1%
1.596446012 3
 
< 0.1%
1.595773993 3
 
< 0.1%
1.593757939 3
 
< 0.1%
1.593085921 3
 
< 0.1%
1.592413902 6
< 0.1%

precip_lag_5
Real number (ℝ)

HIGH CORRELATION 

Distinct404
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010804476
Minimum-0.19875046
Maximum35.186123
Zeros0
Zeros (%)0.0%
Negative12284
Negative (%)88.4%
Memory size108.7 KiB
2024-07-14T18:21:13.328376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.19875046
5-th percentile-0.19875046
Q1-0.19875046
median-0.19875046
Q3-0.19875046
95-th percentile0.88950753
Maximum35.186123
Range35.384873
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1736181
Coefficient of variation (CV)108.62332
Kurtosis297.56151
Mean0.010804476
Median Absolute Deviation (MAD)0
Skewness14.117775
Sum150.14981
Variance1.3773794
MonotonicityNot monotonic
2024-07-14T18:21:14.476306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1943534597 300
 
2.2%
-0.1965519606 213
 
1.5%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1855594557 69
 
0.5%
-0.1877579567 66
 
0.5%
-0.1833609547 54
 
0.4%
0.01010713218 54
 
0.4%
-0.1789639527 36
 
0.3%
Other values (394) 1949
 
14.0%
ValueCountFrequency (%)
-0.1987504616 10952
78.8%
-0.1965519606 213
 
1.5%
-0.1943534597 300
 
2.2%
-0.1921549587 117
 
0.8%
-0.1899564577 87
 
0.6%
-0.1877579567 66
 
0.5%
-0.1855594557 69
 
0.5%
-0.1833609547 54
 
0.4%
-0.1811624537 36
 
0.3%
-0.1789639527 36
 
0.3%
ValueCountFrequency (%)
35.18612293 3
< 0.1%
26.04695433 3
< 0.1%
22.98004545 3
< 0.1%
16.31858746 3
< 0.1%
15.46556907 3
< 0.1%
15.01267787 3
< 0.1%
13.9683899 3
< 0.1%
13.94420639 3
< 0.1%
12.39206469 3
< 0.1%
11.31919621 3
< 0.1%

weekday_Friday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.0
11885 
1.0
2012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11885
85.5%
1.0 2012
 
14.5%

Length

2024-07-14T18:21:14.637872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:14.759558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11885
85.5%
1.0 2012
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 25782
61.8%
. 13897
33.3%
1 2012
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25782
61.8%
. 13897
33.3%
1 2012
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25782
61.8%
. 13897
33.3%
1 2012
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25782
61.8%
. 13897
33.3%
1 2012
 
4.8%

weekday_Monday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.0
11895 
1.0
2002 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 11895
85.6%
1.0 2002
 
14.4%

Length

2024-07-14T18:21:14.884214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:15.000901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11895
85.6%
1.0 2002
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 25792
61.9%
. 13897
33.3%
1 2002
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25792
61.9%
. 13897
33.3%
1 2002
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25792
61.9%
. 13897
33.3%
1 2002
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25792
61.9%
. 13897
33.3%
1 2002
 
4.8%

weekday_Saturday
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.0
11934 
1.0
1963 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11934
85.9%
1.0 1963
 
14.1%

Length

2024-07-14T18:21:15.126608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:15.241257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11934
85.9%
1.0 1963
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 25831
62.0%
. 13897
33.3%
1 1963
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25831
62.0%
. 13897
33.3%
1 1963
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25831
62.0%
. 13897
33.3%
1 1963
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25831
62.0%
. 13897
33.3%
1 1963
 
4.7%

weekday_Sunday
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.0
11968 
1.0
1929 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11968
86.1%
1.0 1929
 
13.9%

Length

2024-07-14T18:21:15.369914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:15.489595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11968
86.1%
1.0 1929
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 25865
62.0%
. 13897
33.3%
1 1929
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25865
62.0%
. 13897
33.3%
1 1929
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25865
62.0%
. 13897
33.3%
1 1929
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25865
62.0%
. 13897
33.3%
1 1929
 
4.6%

weekday_Thursday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.0
11889 
1.0
2008 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11889
85.6%
1.0 2008
 
14.4%

Length

2024-07-14T18:21:15.616255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:15.734937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11889
85.6%
1.0 2008
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 25786
61.9%
. 13897
33.3%
1 2008
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25786
61.9%
. 13897
33.3%
1 2008
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25786
61.9%
. 13897
33.3%
1 2008
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25786
61.9%
. 13897
33.3%
1 2008
 
4.8%

weekday_Tuesday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.0
11896 
1.0
2001 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11896
85.6%
1.0 2001
 
14.4%

Length

2024-07-14T18:21:15.848422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:15.956135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11896
85.6%
1.0 2001
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 25793
61.9%
. 13897
33.3%
1 2001
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25793
61.9%
. 13897
33.3%
1 2001
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25793
61.9%
. 13897
33.3%
1 2001
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25793
61.9%
. 13897
33.3%
1 2001
 
4.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
0.0
11915 
1.0
1982 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41691
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11915
85.7%
1.0 1982
 
14.3%

Length

2024-07-14T18:21:16.065506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:16.173263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11915
85.7%
1.0 1982
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 25812
61.9%
. 13897
33.3%
1 1982
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25812
61.9%
. 13897
33.3%
1 1982
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25812
61.9%
. 13897
33.3%
1 1982
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25812
61.9%
. 13897
33.3%
1 1982
 
4.8%

year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
2024
13897 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters55588
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 13897
100.0%

Length

2024-07-14T18:21:16.300877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:16.409586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2024 13897
100.0%

Most occurring characters

ValueCountFrequency (%)
2 27794
50.0%
0 13897
25.0%
4 13897
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 27794
50.0%
0 13897
25.0%
4 13897
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 27794
50.0%
0 13897
25.0%
4 13897
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 27794
50.0%
0 13897
25.0%
4 13897
25.0%

month
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7175649
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.7 KiB
2024-07-14T18:21:16.502339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8611186
Coefficient of variation (CV)0.5006284
Kurtosis-1.1648761
Mean3.7175649
Median Absolute Deviation (MAD)2
Skewness0.056281293
Sum51663
Variance3.4637624
MonotonicityIncreasing
2024-07-14T18:21:16.622018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2227
16.0%
5 2222
16.0%
3 2192
15.8%
4 2155
15.5%
6 2137
15.4%
2 2088
15.0%
7 876
 
6.3%
ValueCountFrequency (%)
1 2227
16.0%
2 2088
15.0%
3 2192
15.8%
4 2155
15.5%
5 2222
16.0%
6 2137
15.4%
7 876
 
6.3%
ValueCountFrequency (%)
7 876
 
6.3%
6 2137
15.4%
5 2222
16.0%
4 2155
15.5%
3 2192
15.8%
2 2088
15.0%
1 2227
16.0%

day
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.141182
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.7 KiB
2024-07-14T18:21:16.761645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.8050391
Coefficient of variation (CV)0.58152919
Kurtosis-1.2025334
Mean15.141182
Median Absolute Deviation (MAD)8
Skewness0.094311548
Sum210417
Variance77.528713
MonotonicityNot monotonic
2024-07-14T18:21:16.913241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 504
 
3.6%
7 504
 
3.6%
3 501
 
3.6%
2 501
 
3.6%
9 501
 
3.6%
4 500
 
3.6%
10 498
 
3.6%
12 498
 
3.6%
8 498
 
3.6%
1 495
 
3.6%
Other values (21) 8897
64.0%
ValueCountFrequency (%)
1 495
3.6%
2 501
3.6%
3 501
3.6%
4 500
3.6%
5 504
3.6%
6 472
3.4%
7 504
3.6%
8 498
3.6%
9 501
3.6%
10 498
3.6%
ValueCountFrequency (%)
31 213
1.5%
30 358
2.6%
29 432
3.1%
28 432
3.1%
27 432
3.1%
26 432
3.1%
25 429
3.1%
24 429
3.1%
23 432
3.1%
22 432
3.1%

hour
Real number (ℝ)

HIGH CORRELATION  UNIFORM  ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.475714
Minimum0
Maximum23
Zeros572
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size108.7 KiB
2024-07-14T18:21:17.057897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9006127
Coefficient of variation (CV)0.60132316
Kurtosis-1.1991568
Mean11.475714
Median Absolute Deviation (MAD)6
Skewness0.0022591163
Sum159478
Variance47.618455
MonotonicityNot monotonic
2024-07-14T18:21:17.201469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 585
 
4.2%
9 585
 
4.2%
11 583
 
4.2%
19 582
 
4.2%
17 582
 
4.2%
16 582
 
4.2%
15 582
 
4.2%
14 582
 
4.2%
20 582
 
4.2%
18 582
 
4.2%
Other values (14) 8070
58.1%
ValueCountFrequency (%)
0 572
4.1%
1 580
4.2%
2 579
4.2%
3 582
4.2%
4 582
4.2%
5 582
4.2%
6 579
4.2%
7 576
4.1%
8 582
4.2%
9 585
4.2%
ValueCountFrequency (%)
23 579
4.2%
22 535
3.8%
21 582
4.2%
20 582
4.2%
19 582
4.2%
18 582
4.2%
17 582
4.2%
16 582
4.2%
15 582
4.2%
14 582
4.2%

location_id
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.7 KiB
476.0
4638 
470.0
4633 
135.0
4626 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters69485
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row476.0
2nd row135.0
3rd row470.0
4th row476.0
5th row135.0

Common Values

ValueCountFrequency (%)
476.0 4638
33.4%
470.0 4633
33.3%
135.0 4626
33.3%

Length

2024-07-14T18:21:17.351069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T18:21:17.486705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
476.0 4638
33.4%
470.0 4633
33.3%
135.0 4626
33.3%

Most occurring characters

ValueCountFrequency (%)
0 18530
26.7%
. 13897
20.0%
4 9271
13.3%
7 9271
13.3%
6 4638
 
6.7%
1 4626
 
6.7%
3 4626
 
6.7%
5 4626
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18530
26.7%
. 13897
20.0%
4 9271
13.3%
7 9271
13.3%
6 4638
 
6.7%
1 4626
 
6.7%
3 4626
 
6.7%
5 4626
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18530
26.7%
. 13897
20.0%
4 9271
13.3%
7 9271
13.3%
6 4638
 
6.7%
1 4626
 
6.7%
3 4626
 
6.7%
5 4626
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18530
26.7%
. 13897
20.0%
4 9271
13.3%
7 9271
13.3%
6 4638
 
6.7%
1 4626
 
6.7%
3 4626
 
6.7%
5 4626
 
6.7%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct377
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.749133
Minimum-11.2
Maximum31.7
Zeros12
Zeros (%)0.1%
Negative1086
Negative (%)7.8%
Memory size108.7 KiB
2024-07-14T18:21:17.650268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-11.2
5-th percentile-1.6
Q16.2
median10.3
Q315.7
95-th percentile22.7
Maximum31.7
Range42.9
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.2012877
Coefficient of variation (CV)0.66994126
Kurtosis-0.13751837
Mean10.749133
Median Absolute Deviation (MAD)4.7
Skewness-0.027521007
Sum149380.7
Variance51.858544
MonotonicityNot monotonic
2024-07-14T18:21:17.817864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1 156
 
1.1%
8.4 153
 
1.1%
7.6 138
 
1.0%
7.7 135
 
1.0%
7.4 132
 
0.9%
6.9 129
 
0.9%
8.7 129
 
0.9%
7.2 126
 
0.9%
7.9 123
 
0.9%
9.2 117
 
0.8%
Other values (367) 12559
90.4%
ValueCountFrequency (%)
-11.2 6
< 0.1%
-11 9
0.1%
-10.9 3
 
< 0.1%
-10.7 3
 
< 0.1%
-10 3
 
< 0.1%
-9.7 3
 
< 0.1%
-9.2 6
< 0.1%
-8.9 3
 
< 0.1%
-8.5 3
 
< 0.1%
-8.1 3
 
< 0.1%
ValueCountFrequency (%)
31.7 3
 
< 0.1%
31.4 3
 
< 0.1%
30.9 12
0.1%
30.7 6
< 0.1%
30.6 3
 
< 0.1%
30.4 3
 
< 0.1%
29.8 3
 
< 0.1%
29.7 6
< 0.1%
29.6 6
< 0.1%
29.3 6
< 0.1%

humidity
Real number (ℝ)

HIGH CORRELATION 

Distinct3068
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.021169
Minimum27.12
Maximum99.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size108.7 KiB
2024-07-14T18:21:17.978391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum27.12
5-th percentile45.05
Q164.82
median78.17
Q387.5
95-th percentile95.11
Maximum99.95
Range72.83
Interquartile range (IQR)22.68

Descriptive statistics

Standard deviation15.518521
Coefficient of variation (CV)0.20685523
Kurtosis-0.43797164
Mean75.021169
Median Absolute Deviation (MAD)10.67
Skewness-0.65757272
Sum1042569.2
Variance240.82451
MonotonicityNot monotonic
2024-07-14T18:21:18.155918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.27 18
 
0.1%
86.77 18
 
0.1%
86.84 18
 
0.1%
91.41 18
 
0.1%
90.72 16
 
0.1%
90.02 15
 
0.1%
87.77 15
 
0.1%
76.44 15
 
0.1%
90.27 15
 
0.1%
91.3 15
 
0.1%
Other values (3058) 13734
98.8%
ValueCountFrequency (%)
27.12 3
< 0.1%
28.48 3
< 0.1%
29.8 3
< 0.1%
29.94 3
< 0.1%
30.81 3
< 0.1%
32.19 3
< 0.1%
32.26 3
< 0.1%
32.4 3
< 0.1%
32.55 3
< 0.1%
32.58 3
< 0.1%
ValueCountFrequency (%)
99.95 3
 
< 0.1%
99.94 3
 
< 0.1%
99.93 3
 
< 0.1%
99.91 9
0.1%
99.9 3
 
< 0.1%
99.88 3
 
< 0.1%
99.87 3
 
< 0.1%
99.84 3
 
< 0.1%
99.83 3
 
< 0.1%
99.82 6
< 0.1%

series
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2907
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean784.85594
Minimum0
Maximum6250
Zeros318
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size108.7 KiB
2024-07-14T18:21:18.330450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q155
median439
Q31272
95-th percentile2516
Maximum6250
Range6250
Interquartile range (IQR)1217

Descriptive statistics

Standard deviation908.069
Coefficient of variation (CV)1.1569881
Kurtosis2.7439335
Mean784.85594
Median Absolute Deviation (MAD)417
Skewness1.5398539
Sum10907143
Variance824589.31
MonotonicityNot monotonic
2024-07-14T18:21:18.496049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 318
 
2.3%
6 125
 
0.9%
7 113
 
0.8%
9 112
 
0.8%
8 103
 
0.7%
12 101
 
0.7%
16 92
 
0.7%
13 89
 
0.6%
11 86
 
0.6%
4 85
 
0.6%
Other values (2897) 12673
91.2%
ValueCountFrequency (%)
0 318
2.3%
1 20
 
0.1%
2 36
 
0.3%
3 62
 
0.4%
4 85
 
0.6%
5 80
 
0.6%
6 125
 
0.9%
7 113
 
0.8%
8 103
 
0.7%
9 112
 
0.8%
ValueCountFrequency (%)
6250 1
< 0.1%
5849 1
< 0.1%
5819 1
< 0.1%
5795 1
< 0.1%
5622 1
< 0.1%
5617 1
< 0.1%
5585 1
< 0.1%
5510 1
< 0.1%
5508 1
< 0.1%
5507 1
< 0.1%

Interactions

2024-07-14T18:20:48.059960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:54.493055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:58.350738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:03.542850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:08.041857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:12.514873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:17.310708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:21.629290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:26.032456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:30.428290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:34.449495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:38.404914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:42.438127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:47.097665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:51.642510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:55.917075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:00.236558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:04.430306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:08.203257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:12.803908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:16.996698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:20.987030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:25.261935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.674133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:34.109308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:38.230249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:43.765444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:48.249489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:54.635673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:58.504328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:03.748302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:08.219362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:12.676105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:17.465330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:21.783880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:26.226936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:30.563885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:34.590120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:38.539553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:42.581744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:47.252249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:51.791113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:56.055705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:00.398092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:04.594868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:08.342841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:12.950520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:17.146298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:21.133637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:25.442452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.818747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:34.258873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:38.366885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:43.973891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:48.429971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:54.810209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:58.645947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:03.925826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:08.377937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:12.847646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:17.623869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:21.969382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:26.389500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:30.711527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:34.740765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:38.687160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:42.729347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:47.397861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:51.947692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:56.205307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:00.549684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:04.747493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:08.494434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:13.092138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:17.285923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:21.304179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:25.610004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.970341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:34.424428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:38.508505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:44.154404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:48.618467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:54.949833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:58.796546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:04.074427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:08.536550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:13.014200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:17.777459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:22.134938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:26.553064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:30.871066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:34.887322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:38.830774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:42.882966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:47.541513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:52.078345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:56.352912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:00.714245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:04.888080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:08.653014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:13.246725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:17.427543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:21.452819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:25.791518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:30.119942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:34.586994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:38.639155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:44.312979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:48.827904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:55.098435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:58.946145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:04.216049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:08.676140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:13.184744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:17.943016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:22.317479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:26.707649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:31.015679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.030942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:38.999323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:43.029543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:47.683099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:52.222991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:56.492540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:00.879804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:05.049650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:08.798650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:13.386353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:17.569171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:21.597398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:26.009934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:30.267583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:34.746567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:38.780777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:44.458592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:49.035388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:55.248064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:59.095744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:04.373626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:08.836711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:13.339330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:18.094613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:22.492016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:26.858248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:31.158298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.170566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:39.155908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:43.185128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:48.430104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:52.353648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:56.637186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:01.042371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:05.188280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:08.941276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:13.534953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:17.709831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:21.742009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:26.170505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:30.405179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:34.916151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:38.916414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:44.602209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:49.196955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:55.386664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:59.241355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:04.530208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:08.982322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:13.491966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:18.251325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:22.639626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.006853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:31.300917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.308196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:39.316495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:43.337724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:48.592665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:52.486252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:56.776819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:01.182994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:05.332892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:09.087848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:13.694529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:17.863377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:21.883628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:26.311129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:30.546802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:35.067709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:39.854903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:44.743875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:49.369457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:55.565188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:59.378987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:04.700753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:09.122945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:13.641521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:18.421870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:22.808139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.133550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:31.442538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.437851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:39.461091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:43.486324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:48.763211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:52.611917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:56.922431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:01.342566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:05.478503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:09.246424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:13.866071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:17.995025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:22.015280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:26.478681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:30.712356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:35.223292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:39.978573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:44.883455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:49.538006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:55.735731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:59.503653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:04.884262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:09.258581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:14.155149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:18.572469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:22.969706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.263163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:31.593137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.561522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:39.596726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:43.629938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:48.901838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:52.737578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:57.057027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:01.493165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:05.605165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:09.376078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:14.010684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:18.116704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:22.140940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:26.639250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:30.867940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:35.378878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:40.081298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:45.035088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:49.779359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:55.893345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:59.642319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:05.104675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:09.416161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:14.285799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:18.730044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:23.137257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.420741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:31.765671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.710122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:39.755303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:43.795496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:49.051437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:52.923111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:57.231561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:01.662709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:05.758755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:09.524708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:14.177238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:18.343098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:22.334423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:26.807838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:31.030506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:35.557399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:40.192003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:45.216600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:50.014730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.034930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:59.794874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:05.314113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:09.577729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:14.448363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:18.900589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:23.297719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.559372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:31.945194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.846756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:39.914877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:43.957068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:49.185081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:53.088641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:57.378167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:01.829265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:05.891398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:09.662312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:14.346782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:18.491706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:22.537879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:26.982332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:31.193072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:35.718966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:40.300711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:45.394089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:50.181289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.162588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:59.943478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:05.463714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:09.728325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:14.600958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:19.038221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:23.494192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.683039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:32.083820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:35.974415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:40.042536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:44.102676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:49.309749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:53.235248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:57.517835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:01.972878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.009085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:09.806925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:14.493427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:18.633323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:22.684488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:27.150883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:31.350650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:35.884524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:40.412416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:45.537706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:50.403689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.304255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:00.122028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:05.637248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:09.899867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:14.769506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:19.194839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:23.656756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.831644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:32.233423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:36.121023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:40.187148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:44.260256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:49.447379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:53.426735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:57.668391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:02.159381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.141728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:09.973481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:14.643989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:18.787909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:22.859021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:27.327410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:31.529173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:36.055104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:40.562014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:45.719220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:50.590193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.433894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:00.260628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:05.795826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:10.063430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:14.923093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:19.334459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:23.823312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:27.969276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:32.377036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:36.256662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:40.333757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:44.431796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:49.576035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:53.594287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:57.834953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:02.306986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.286342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:10.159021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:14.784613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:18.921551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:22.998646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:27.552808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:31.701711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:36.192699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:40.773448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:45.901731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:50.776692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.566508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:00.429214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:05.946420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:10.249932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:15.067706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:19.471063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:23.967924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:28.088957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:32.506688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:36.400277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:40.479366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:44.594360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:49.701701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:53.752863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:58.011511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:02.446611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.404028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:10.311576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:14.925236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:19.056193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:23.141265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:27.774217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:31.850316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:36.318363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:40.976904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:46.035410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:50.965187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.701195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:01.631998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:06.107990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:10.414491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:15.220299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:19.624653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:24.200339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:28.232570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:32.644324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:36.549878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:40.646919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:44.793827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:49.886207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:53.921412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:58.204989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:02.591225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.550637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:11.128390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:15.200498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:19.212775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:23.296849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:27.973682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:32.009887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:36.484918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:41.208287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:46.180984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:51.145735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.841773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:01.774578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:06.292495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:10.578052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:15.372891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:19.790212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:24.353893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:28.364219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:32.798911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:36.686512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:40.829429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:44.968359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:50.027863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:54.088965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:58.379491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:02.738829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.680287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:11.251062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:15.376031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:19.359381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:23.462409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:28.176141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:32.165469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:36.625541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:41.410746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:46.322606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:51.301290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:56.972423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:01.906227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:06.456060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:10.767546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:15.540442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:19.941805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:24.497508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:28.486930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:32.945518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:36.826136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:40.961079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:45.124939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:50.160502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:54.236571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:58.547043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:02.899402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.803958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:11.364762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:15.515656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:19.482054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:23.620021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:28.316766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:32.359950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:36.766168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:41.603230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:46.452259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:51.487790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:57.109057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:02.052871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:06.612639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:10.929119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:15.687051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:20.126312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:24.663064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:28.619535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:33.085144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:37.061509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:41.114667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:45.303463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:50.312103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:54.404121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:58.730550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:03.048004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:06.945578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:11.485435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:15.655318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:19.628662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:23.766593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:28.460381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:32.565403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:36.913772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:41.825634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:46.600861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:51.831900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:57.245692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:02.192461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:06.763237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:11.142544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:15.853605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:20.306827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:24.831667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:28.774122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:33.218785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:37.210112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:41.324108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:45.479991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:50.438728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:54.593614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:58.896107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:03.189625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:07.078223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:11.703851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:15.788924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:19.772277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:23.927163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:28.603997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:32.751904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:37.053398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:42.039062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:46.770444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:51.980472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:57.372396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:02.340067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:06.893888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:11.352981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:16.007224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:20.451443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:24.986254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:28.918735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:33.359409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:37.343755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:41.477695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:45.667489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:50.562400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:54.738229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:59.049697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:03.315287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:07.286669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:11.843480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:15.909604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:19.897943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:24.078757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:28.731654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:32.924439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:37.184048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:42.214592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:46.909036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:52.143040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:57.516034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:02.500636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:07.034511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:11.519537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:16.176741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:20.620023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:25.131900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:29.205969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:33.588795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:37.480388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:41.637306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:45.856014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:50.709042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:54.899795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:59.207275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:03.452923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:07.444273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:11.999063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:16.051226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:20.062501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:24.230352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:28.876269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:33.110942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:37.322680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:42.406080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:47.186296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:52.311589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:57.656593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:02.687139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:07.179123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:11.661159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:16.342297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:20.780564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:25.266504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:29.844845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:33.751362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:37.628990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:41.776897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:46.031515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:50.847636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:55.087295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:59.347900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:03.592545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:07.574894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:12.138731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:16.201821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:20.238032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:24.397904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.007959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:33.290460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:37.464297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:42.693312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:47.346865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:52.484127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:57.809185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:02.865662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:07.325282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:11.844665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:16.533785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:20.933154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:25.410120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:29.958550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:33.903981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:37.811501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:41.923506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:46.216022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:51.068047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:55.355577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:59.619173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:03.723243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:07.711529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:12.272331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:16.368378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:20.394610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:24.584404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.138567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:33.461044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:37.672740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:42.875824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:47.488523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:52.647689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:57.936843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:03.000342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:07.447924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:12.004239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:16.694356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:21.114669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:25.529799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:30.061265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:34.034602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:37.952125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:42.046176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:46.481314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:51.218644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:55.507172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:59.768774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:03.927649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:07.828245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:12.397996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:16.514983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:20.541219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:24.773726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.261240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:33.614596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:37.824372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:43.046369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:47.616145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:52.816240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:58.068493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:03.138929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:07.571095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:12.169796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:16.964633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:21.313171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:25.658491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:30.172931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:34.173232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:38.108709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:42.172837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:46.676789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:51.354280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:55.640843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:59.921366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:04.099191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:07.948894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:12.525652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:16.674556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:20.675862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:24.928826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.385906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:33.766189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:37.956024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:43.279743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:47.752780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:53.007725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:18:58.205170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:03.316457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:07.860322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:12.350313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:17.147145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:21.477696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:25.844956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:30.306575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:34.316850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:38.262297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:42.311510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:46.906175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:51.503915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:19:55.783432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:00.083959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:04.278713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:08.080544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:12.666304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:16.839141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:20.852386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:25.092388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:29.524534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:33.953687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:38.096607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:43.531071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T18:20:47.898390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-14T18:21:18.722401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
dayeventevent_lag_1event_lag_2event_lag_3event_lag_4event_lag_5event_lead_1event_lead_2event_lead_3event_lead_4event_lead_5green_marketholidayholiday_lag_1holiday_lag_2holiday_lag_3holiday_lag_4holiday_lag_5holiday_lead_1holiday_lead_2holiday_lead_3holiday_lead_4holiday_lead_5hourhumidityhumidity_lag_1humidity_lag_2humidity_lag_3humidity_lag_4humidity_lag_5location_idmonthpedestrians_count_lag_1pedestrians_count_lag_2pedestrians_count_lag_3pedestrians_count_lag_4pedestrians_count_lag_5precipprecip_lag_1precip_lag_2precip_lag_3precip_lag_4precip_lag_5seriesspecialities_markettemptemp_lag_1temp_lag_2temp_lag_3temp_lag_4temp_lag_5weekday_Fridayweekday_Mondayweekday_Saturdayweekday_Sundayweekday_Thursdayweekday_Tuesdayweekday_Wednesdayworkdayworkday_lag_1workday_lag_2workday_lag_3workday_lag_4workday_lag_5workday_lead_1workday_lead_2workday_lead_3workday_lead_4workday_lead_5
day1.0000.2570.2560.2560.2560.2550.2550.2560.2550.2550.2540.2540.0530.2330.2340.2340.2340.2350.2350.2330.2330.2330.2330.2330.000-0.049-0.049-0.049-0.050-0.050-0.0500.000-0.107-0.010-0.010-0.010-0.010-0.011-0.049-0.049-0.050-0.050-0.050-0.051-0.0100.053-0.039-0.039-0.039-0.040-0.040-0.0400.0760.0860.0580.0980.0980.0460.0780.0640.0630.0620.0610.0600.0600.0630.0620.0620.0610.061
event0.2571.0000.9980.9960.9940.9920.9900.9980.9960.9940.9920.9900.0090.0720.0710.0710.0700.0690.0690.0720.0720.0710.0710.071-0.008-0.025-0.025-0.025-0.024-0.024-0.0240.0000.2880.0130.0130.0120.0120.012-0.001-0.001-0.001-0.002-0.002-0.0020.0140.0090.2150.2150.2150.2150.2150.2150.0150.0570.0750.0600.0180.0570.0150.1050.1040.1020.1010.0990.0980.1050.1040.1040.1030.103
event_lag_10.2560.9981.0000.9980.9960.9940.9920.9960.9940.9920.9900.9870.0070.0720.0720.0710.0710.0700.0690.0720.0720.0710.0710.071-0.007-0.025-0.025-0.025-0.025-0.024-0.0240.0000.2880.0130.0130.0130.0120.012-0.001-0.001-0.001-0.001-0.002-0.0020.0140.0070.2150.2150.2150.2150.2150.2150.0140.0550.0740.0610.0180.0570.0150.1050.1050.1040.1020.1000.0990.1040.1040.1030.1030.102
event_lag_20.2560.9960.9981.0000.9980.9960.9940.9940.9920.9900.9870.9850.0050.0720.0720.0720.0710.0710.0700.0720.0720.0710.0710.071-0.007-0.026-0.025-0.025-0.025-0.024-0.0240.0000.2880.0130.0130.0130.0120.012-0.001-0.001-0.001-0.001-0.001-0.0020.0140.0050.2150.2150.2150.2150.2150.2150.0140.0540.0730.0610.0170.0570.0160.1040.1040.1050.1030.1020.1000.1030.1030.1020.1020.101
event_lag_30.2560.9940.9960.9981.0000.9980.9960.9920.9900.9870.9850.9830.0000.0720.0720.0720.0720.0710.0710.0720.0710.0710.0710.071-0.007-0.026-0.026-0.025-0.025-0.025-0.0240.0000.2880.0130.0130.0130.0130.012-0.000-0.000-0.000-0.000-0.001-0.0010.0140.0000.2150.2150.2150.2150.2150.2150.0130.0520.0720.0610.0170.0570.0160.1030.1040.1040.1050.1030.1020.1030.1020.1020.1010.101
event_lag_40.2550.9920.9940.9960.9981.0000.9980.9900.9870.9850.9830.9810.0000.0720.0720.0720.0720.0720.0710.0720.0710.0710.0710.071-0.007-0.026-0.026-0.026-0.025-0.025-0.0250.0000.2880.0140.0130.0130.0130.013-0.000-0.000-0.000-0.000-0.000-0.0010.0140.0000.2160.2150.2150.2150.2150.2150.0130.0500.0710.0610.0170.0570.0170.1030.1030.1030.1040.1040.1030.1020.1020.1010.1010.100
event_lag_50.2550.9900.9920.9940.9960.9981.0000.9870.9850.9830.9810.9790.0000.0720.0720.0720.0720.0720.0720.0720.0710.0710.0710.071-0.006-0.027-0.026-0.026-0.025-0.025-0.0250.0000.2870.0140.0130.0130.0130.013-0.000-0.000-0.000-0.000-0.000-0.0000.0140.0000.2160.2150.2150.2150.2150.2150.0120.0490.0700.0610.0170.0570.0170.1020.1020.1030.1030.1040.1040.1010.1010.1000.1000.099
event_lead_10.2560.9980.9960.9940.9920.9900.9871.0000.9980.9960.9940.9920.0100.0710.0700.0700.0690.0690.0680.0720.0720.0720.0710.071-0.007-0.025-0.025-0.025-0.024-0.024-0.0240.0000.2880.0130.0120.0120.0120.012-0.001-0.002-0.002-0.002-0.002-0.0020.0130.0100.2150.2150.2150.2150.2150.2150.0160.0570.0750.0590.0170.0560.0150.1040.1020.1010.0990.0980.0960.1050.1050.1040.1040.103
event_lead_20.2550.9960.9940.9920.9900.9870.9850.9981.0000.9980.9960.9940.0110.0700.0700.0690.0680.0680.0670.0710.0720.0720.0710.071-0.007-0.025-0.025-0.025-0.024-0.024-0.0240.0000.2890.0130.0120.0120.0120.012-0.002-0.002-0.002-0.002-0.002-0.0010.0130.0110.2150.2150.2150.2150.2150.2150.0170.0570.0750.0570.0170.0560.0150.1030.1010.0990.0980.0960.0950.1040.1060.1050.1050.104
event_lead_30.2550.9940.9920.9900.9870.9850.9830.9960.9981.0000.9980.9960.0120.0700.0690.0680.0680.0670.0660.0700.0710.0720.0710.071-0.007-0.025-0.025-0.025-0.025-0.025-0.0250.0000.2890.0130.0130.0130.0130.013-0.002-0.002-0.002-0.002-0.001-0.0010.0130.0120.2150.2150.2150.2150.2150.2150.0180.0570.0750.0550.0160.0560.0150.1010.1000.0980.0970.0950.0940.1030.1040.1060.1050.105
event_lead_40.2540.9920.9900.9870.9850.9830.9810.9940.9960.9981.0000.9980.0130.0690.0680.0680.0670.0660.0660.0700.0700.0710.0720.071-0.007-0.025-0.025-0.025-0.025-0.025-0.0250.0000.2890.0130.0130.0130.0130.013-0.002-0.002-0.002-0.002-0.001-0.0010.0130.0130.2150.2150.2150.2150.2150.2150.0190.0570.0740.0540.0160.0550.0150.1000.0980.0970.0950.0940.0920.1010.1030.1040.1060.105
event_lead_50.2540.9900.9870.9850.9830.9810.9790.9920.9940.9960.9981.0000.0140.0680.0680.0670.0660.0660.0650.0690.0700.0700.0710.071-0.006-0.025-0.025-0.025-0.025-0.025-0.0250.0000.2890.0130.0130.0130.0130.013-0.002-0.002-0.002-0.001-0.001-0.0000.0130.0140.2150.2150.2150.2150.2150.2150.0200.0570.0740.0520.0150.0550.0150.0990.0970.0950.0940.0920.0910.1000.1020.1030.1050.106
green_market0.0530.0090.0070.0050.0000.0000.0000.0100.0110.0120.0130.0141.0000.1220.1200.1170.1150.1130.1110.1200.1180.1150.1130.111-0.0030.0100.0080.0060.0050.0030.0020.0000.0050.0970.0960.0940.0920.091-0.056-0.056-0.056-0.056-0.057-0.0580.0981.000-0.005-0.005-0.006-0.006-0.006-0.0060.3550.4750.3500.4640.4750.3540.3520.0850.0940.1030.1120.1210.1300.0770.0680.0590.0500.041
holiday0.2330.0720.0720.0720.0720.0720.0720.0710.0700.0700.0690.0680.1221.0000.9890.9780.9680.9570.9470.9890.9780.9680.9570.9460.0050.0010.0020.0020.0030.0040.0040.000-0.014-0.058-0.058-0.057-0.057-0.0560.0120.0130.0130.0130.0140.014-0.0580.1220.0030.0020.0020.0020.0010.0010.0140.0740.0410.0170.0780.1040.0420.0150.0140.0140.0130.0120.0110.0150.0150.0150.0150.016
holiday_lag_10.2340.0710.0720.0720.0720.0720.0720.0700.0700.0690.0680.0680.1200.9891.0000.9890.9780.9680.9570.9780.9680.9570.9460.9360.0040.0000.0010.0020.0030.0030.0040.000-0.014-0.058-0.058-0.058-0.057-0.0570.0110.0120.0120.0130.0130.013-0.0590.1200.0030.0020.0020.0020.0010.0010.0150.0740.0400.0160.0760.1020.0430.0150.0150.0150.0140.0130.0120.0160.0160.0160.0160.016
holiday_lag_20.2340.0710.0710.0720.0720.0720.0720.0700.0690.0680.0680.0670.1170.9780.9891.0000.9890.9780.9680.9680.9570.9460.9360.9250.004-0.0010.0000.0010.0020.0030.0040.000-0.015-0.059-0.058-0.058-0.057-0.0570.0100.0110.0120.0120.0130.013-0.0600.1170.0030.0030.0020.0020.0010.0010.0160.0730.0390.0140.0740.0990.0440.0160.0160.0160.0150.0140.0130.0160.0160.0160.0160.016
holiday_lag_30.2340.0700.0710.0710.0720.0720.0720.0690.0680.0680.0670.0660.1150.9680.9780.9891.0000.9890.9780.9570.9460.9360.9250.9150.003-0.001-0.0000.0000.0010.0020.0030.000-0.015-0.060-0.059-0.058-0.058-0.0570.0090.0100.0110.0120.0120.012-0.0610.1150.0030.0030.0020.0020.0010.0010.0170.0730.0380.0130.0720.0970.0440.0160.0160.0160.0160.0150.0140.0160.0160.0160.0160.016
holiday_lag_40.2350.0690.0700.0710.0710.0720.0720.0690.0680.0670.0660.0660.1130.9570.9680.9780.9891.0000.9890.9470.9360.9250.9150.9040.003-0.002-0.001-0.0000.0010.0010.0020.000-0.015-0.061-0.060-0.059-0.058-0.0580.0090.0090.0100.0110.0120.012-0.0610.1130.0030.0030.0020.0020.0020.0010.0180.0730.0380.0120.0700.0940.0450.0160.0160.0160.0160.0160.0150.0160.0160.0160.0160.016
holiday_lag_50.2350.0690.0690.0700.0710.0710.0720.0680.0670.0660.0660.0650.1110.9470.9570.9680.9780.9891.0000.9360.9250.9150.9040.8940.002-0.003-0.002-0.001-0.0000.0010.0020.000-0.015-0.062-0.061-0.060-0.059-0.0580.0090.0090.0090.0100.0110.012-0.0620.1110.0030.0030.0030.0020.0020.0010.0180.0730.0370.0110.0680.0920.0460.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.017
holiday_lead_10.2330.0720.0720.0720.0720.0720.0720.0720.0710.0700.0700.0690.1200.9890.9780.9680.9570.9470.9361.0000.9890.9780.9680.9570.0040.0020.0020.0030.0030.0040.0040.000-0.014-0.058-0.058-0.057-0.057-0.0560.0130.0130.0130.0140.0140.014-0.0580.1200.0030.0020.0020.0020.0010.0010.0130.0710.0400.0170.0770.1030.0400.0140.0130.0130.0120.0110.0100.0150.0150.0150.0150.015
holiday_lead_20.2330.0720.0720.0720.0710.0710.0710.0720.0720.0710.0700.0700.1180.9780.9680.9570.9460.9360.9250.9891.0000.9890.9780.9680.0040.0020.0030.0030.0040.0040.0040.000-0.014-0.058-0.058-0.057-0.056-0.0560.0130.0140.0140.0140.0150.015-0.0580.1180.0030.0020.0020.0020.0010.0010.0130.0690.0390.0170.0760.1020.0380.0130.0120.0120.0110.0100.0090.0140.0150.0150.0150.015
holiday_lead_30.2330.0710.0710.0710.0710.0710.0710.0720.0720.0720.0710.0700.1150.9680.9570.9460.9360.9250.9150.9780.9891.0000.9890.9780.0030.0030.0030.0030.0040.0040.0040.000-0.014-0.058-0.057-0.056-0.056-0.0560.0140.0140.0140.0150.0150.015-0.0580.1150.0020.0020.0020.0020.0010.0010.0120.0670.0380.0170.0760.1020.0360.0120.0110.0110.0100.0090.0080.0130.0140.0150.0150.015
holiday_lead_40.2330.0710.0710.0710.0710.0710.0710.0710.0710.0710.0720.0710.1130.9570.9460.9360.9250.9150.9040.9680.9780.9891.0000.9890.0030.0030.0030.0040.0030.0030.0030.000-0.014-0.057-0.057-0.056-0.056-0.0550.0140.0150.0150.0150.0150.016-0.0580.1130.0020.0020.0020.0020.0010.0010.0110.0640.0370.0170.0750.1010.0340.0110.0100.0090.0080.0070.0060.0120.0130.0140.0150.015
holiday_lead_50.2330.0710.0710.0710.0710.0710.0710.0710.0710.0710.0710.0710.1110.9460.9360.9250.9150.9040.8940.9570.9680.9780.9891.0000.0020.0030.0030.0030.0030.0030.0030.000-0.014-0.057-0.057-0.056-0.056-0.0550.0150.0150.0150.0160.0160.016-0.0570.1110.0030.0020.0020.0020.0010.0010.0100.0620.0360.0170.0740.1000.0320.0100.0090.0080.0070.0060.0050.0110.0120.0130.0140.014
hour0.000-0.008-0.007-0.007-0.007-0.007-0.006-0.007-0.007-0.007-0.007-0.006-0.0030.0050.0040.0040.0030.0030.0020.0040.0040.0030.0030.0021.000-0.384-0.392-0.400-0.408-0.407-0.4050.000-0.0120.5310.5760.6290.6450.6570.0290.0280.0280.0270.0260.0260.4750.0000.2030.2080.2130.2180.2190.2190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
humidity-0.049-0.025-0.025-0.026-0.026-0.026-0.027-0.025-0.025-0.025-0.025-0.0250.0100.0010.000-0.001-0.001-0.002-0.0030.0020.0020.0030.0030.003-0.3841.0000.9810.9620.9430.9130.8840.000-0.183-0.504-0.520-0.535-0.538-0.5420.3630.3630.3630.3620.3570.351-0.4890.046-0.455-0.449-0.444-0.438-0.427-0.4150.0870.0930.1280.0760.1260.1200.0670.1080.1070.1050.1040.1020.1000.1100.1120.1130.1150.118
humidity_lag_1-0.049-0.025-0.025-0.025-0.026-0.026-0.026-0.025-0.025-0.025-0.025-0.0250.0080.0020.0010.000-0.000-0.001-0.0020.0020.0030.0030.0030.003-0.3920.9811.0000.9810.9620.9430.9130.000-0.183-0.489-0.504-0.519-0.535-0.5380.3490.3630.3630.3630.3620.356-0.4610.046-0.448-0.455-0.449-0.444-0.438-0.4270.0870.0930.1290.0760.1260.1200.0690.1100.1090.1070.1050.1040.1020.1120.1140.1160.1180.120
humidity_lag_2-0.049-0.025-0.025-0.025-0.025-0.026-0.026-0.025-0.025-0.025-0.025-0.0250.0060.0020.0020.0010.000-0.000-0.0010.0030.0030.0030.0040.003-0.4000.9620.9811.0000.9810.9620.9430.000-0.183-0.461-0.489-0.504-0.519-0.5350.3340.3490.3630.3630.3630.362-0.4370.046-0.442-0.448-0.455-0.449-0.444-0.4380.0880.0930.1310.0760.1260.1210.0710.1120.1100.1090.1070.1050.1040.1140.1160.1180.1200.121
humidity_lag_3-0.050-0.024-0.025-0.025-0.025-0.025-0.025-0.024-0.024-0.025-0.025-0.0250.0050.0030.0030.0020.0010.001-0.0000.0030.0040.0040.0030.003-0.4080.9430.9620.9811.0000.9810.9620.000-0.183-0.437-0.461-0.489-0.504-0.5190.3190.3340.3490.3630.3630.363-0.4110.047-0.435-0.442-0.448-0.455-0.449-0.4440.0880.0930.1320.0760.1270.1220.0720.1140.1120.1100.1090.1070.1050.1160.1180.1200.1210.122
humidity_lag_4-0.050-0.024-0.024-0.024-0.025-0.025-0.025-0.024-0.024-0.025-0.025-0.0250.0030.0040.0030.0030.0020.0010.0010.0040.0040.0040.0030.003-0.4070.9130.9430.9620.9811.0000.9810.000-0.183-0.411-0.437-0.461-0.489-0.5040.3040.3190.3340.3490.3630.363-0.3730.047-0.423-0.435-0.442-0.448-0.455-0.4490.0880.0950.1340.0780.1270.1220.0730.1160.1140.1120.1100.1090.1070.1180.1210.1210.1220.123
humidity_lag_5-0.050-0.024-0.024-0.024-0.024-0.025-0.025-0.024-0.024-0.025-0.025-0.0250.0020.0040.0040.0040.0030.0020.0020.0040.0040.0040.0030.003-0.4050.8840.9130.9430.9620.9811.0000.000-0.183-0.373-0.411-0.436-0.461-0.4890.2880.3040.3190.3340.3480.363-0.3420.048-0.411-0.423-0.435-0.442-0.448-0.4550.0880.0960.1350.0800.1270.1230.0740.1180.1160.1140.1120.1100.1090.1210.1210.1220.1230.123
location_id0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.001-0.1050.0930.012-0.1040.0930.0000.0000.0010.0000.0000.0000.0120.0000.0010.0010.0010.0010.0010.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
month-0.1070.2880.2880.2880.2880.2880.2870.2880.2890.2890.2890.2890.005-0.014-0.014-0.015-0.015-0.015-0.015-0.014-0.014-0.014-0.014-0.014-0.012-0.183-0.183-0.183-0.183-0.183-0.1830.0011.0000.0370.0370.0370.0360.0360.0180.0180.0180.0190.0190.0190.0380.0140.7810.7810.7810.7810.7810.7810.0350.0360.0430.0570.0440.0380.0420.0790.0790.0780.0780.0770.0770.0780.0780.0770.0760.075
pedestrians_count_lag_1-0.0100.0130.0130.0130.0130.0140.0140.0130.0130.0130.0130.0130.097-0.058-0.058-0.059-0.060-0.061-0.062-0.058-0.058-0.058-0.057-0.0570.531-0.504-0.489-0.461-0.437-0.411-0.373-0.1050.0371.0000.8980.8830.9290.824-0.021-0.020-0.019-0.017-0.017-0.0160.8980.1540.3020.2940.2780.2650.2510.2290.0610.0650.2340.2190.0430.0690.0570.2060.2050.2050.2050.2050.2050.2060.2060.2060.2060.206
pedestrians_count_lag_2-0.0100.0130.0130.0130.0130.0130.0130.0120.0120.0130.0130.0130.096-0.058-0.058-0.058-0.059-0.060-0.061-0.058-0.058-0.057-0.057-0.0570.576-0.520-0.504-0.489-0.461-0.437-0.4110.0930.0370.8981.0000.8980.8830.929-0.023-0.021-0.020-0.019-0.017-0.0170.8830.1550.3100.3020.2940.2780.2650.2510.0610.0660.2340.2190.0430.0690.0570.2060.2060.2060.2060.2060.2060.2060.2060.2060.2060.206
pedestrians_count_lag_3-0.0100.0120.0130.0130.0130.0130.0130.0120.0120.0130.0130.0130.094-0.057-0.058-0.058-0.058-0.059-0.060-0.057-0.057-0.056-0.056-0.0560.629-0.535-0.519-0.504-0.489-0.461-0.4360.0120.0370.8830.8981.0000.8980.883-0.024-0.023-0.021-0.020-0.019-0.0170.9290.1540.3180.3100.3020.2940.2780.2650.0610.0650.2340.2190.0430.0690.0570.2060.2060.2060.2060.2060.2060.2060.2060.2060.2060.205
pedestrians_count_lag_4-0.0100.0120.0120.0120.0130.0130.0130.0120.0120.0130.0130.0130.092-0.057-0.057-0.057-0.058-0.058-0.059-0.057-0.056-0.056-0.056-0.0560.645-0.538-0.535-0.519-0.504-0.489-0.461-0.1040.0360.9290.8830.8981.0000.898-0.024-0.024-0.023-0.021-0.020-0.0180.8240.1540.3190.3180.3100.3010.2940.2780.0610.0660.2350.2190.0430.0690.0570.2060.2060.2060.2060.2060.2060.2060.2060.2060.2050.205
pedestrians_count_lag_5-0.0110.0120.0120.0120.0120.0130.0130.0120.0120.0130.0130.0130.091-0.056-0.057-0.057-0.057-0.058-0.058-0.056-0.056-0.056-0.055-0.0550.657-0.542-0.538-0.535-0.519-0.504-0.4890.0930.0360.8240.9290.8830.8981.000-0.022-0.024-0.024-0.023-0.021-0.0200.8020.1540.3220.3190.3170.3090.3010.2930.0610.0660.2350.2190.0420.0690.0570.2070.2070.2060.2060.2060.2060.2060.2060.2060.2050.204
precip-0.049-0.001-0.001-0.001-0.000-0.000-0.000-0.001-0.002-0.002-0.002-0.002-0.0560.0120.0110.0100.0090.0090.0090.0130.0130.0140.0140.0150.0290.3630.3490.3340.3190.3040.2880.0000.018-0.021-0.023-0.024-0.024-0.0221.0000.9130.8270.7400.6930.645-0.0200.028-0.014-0.011-0.008-0.005-0.0020.0010.0080.0150.0420.0170.0460.0000.0360.0170.0170.0170.0170.0170.0170.0170.0170.0170.0170.017
precip_lag_1-0.049-0.001-0.001-0.001-0.000-0.000-0.000-0.002-0.002-0.002-0.002-0.002-0.0560.0130.0120.0110.0100.0090.0090.0130.0140.0140.0150.0150.0280.3630.3630.3490.3340.3190.3040.0000.018-0.020-0.021-0.023-0.024-0.0240.9131.0000.9130.8270.7400.693-0.0190.017-0.014-0.013-0.011-0.008-0.005-0.0020.0000.0150.0310.0180.0430.0000.0210.0170.0170.0170.0170.0170.0170.0170.0170.0170.0170.016
precip_lag_2-0.050-0.001-0.001-0.001-0.000-0.000-0.000-0.002-0.002-0.002-0.002-0.002-0.0560.0130.0120.0120.0110.0100.0090.0130.0140.0140.0150.0150.0280.3630.3630.3630.3490.3340.3190.0010.018-0.019-0.020-0.021-0.023-0.0240.8270.9131.0000.9130.8270.740-0.0170.016-0.013-0.013-0.013-0.011-0.008-0.0050.0000.0150.0240.0280.0450.0000.0000.0170.0170.0170.0170.0170.0170.0170.0170.0170.0160.016
precip_lag_3-0.050-0.002-0.001-0.001-0.000-0.000-0.000-0.002-0.002-0.002-0.002-0.001-0.0560.0130.0130.0120.0120.0110.0100.0140.0140.0150.0150.0160.0270.3620.3630.3630.3630.3490.3340.0000.019-0.017-0.019-0.020-0.021-0.0230.7400.8270.9131.0000.9130.827-0.0170.026-0.013-0.013-0.013-0.013-0.010-0.0080.0010.0150.0230.0400.0510.0000.0000.0170.0170.0170.0170.0170.0170.0170.0170.0160.0160.016
precip_lag_4-0.050-0.002-0.002-0.001-0.001-0.000-0.000-0.002-0.002-0.001-0.001-0.001-0.0570.0140.0130.0130.0120.0120.0110.0140.0150.0150.0150.0160.0260.3570.3620.3630.3630.3630.3480.0000.019-0.017-0.017-0.019-0.020-0.0210.6930.7400.8270.9131.0000.913-0.0160.025-0.013-0.013-0.013-0.013-0.013-0.0100.0000.0150.0210.0400.0510.0000.0000.0170.0170.0170.0170.0170.0170.0170.0160.0160.0160.016
precip_lag_5-0.051-0.002-0.002-0.002-0.001-0.001-0.000-0.002-0.001-0.001-0.001-0.000-0.0580.0140.0130.0130.0120.0120.0120.0140.0150.0150.0160.0160.0260.3510.3560.3620.3630.3630.3630.0000.019-0.016-0.017-0.017-0.018-0.0200.6450.6930.7400.8270.9131.000-0.0160.025-0.013-0.013-0.013-0.013-0.013-0.0130.0000.0150.0190.0400.0520.0000.0000.0170.0170.0170.0170.0170.0170.0160.0160.0160.0160.018
series-0.0100.0140.0140.0140.0140.0140.0140.0130.0130.0130.0130.0130.098-0.058-0.059-0.060-0.061-0.061-0.062-0.058-0.058-0.058-0.058-0.0570.475-0.489-0.461-0.437-0.411-0.373-0.3420.0120.0380.8980.8830.9290.8240.802-0.020-0.019-0.017-0.017-0.016-0.0161.0000.1550.2940.2780.2650.2510.2290.2120.0610.0660.2350.2190.0420.0690.0570.2060.2060.2060.2060.2060.2060.2060.2060.2060.2060.206
specialities_market0.0530.0090.0070.0050.0000.0000.0000.0100.0110.0120.0130.0141.0000.1220.1200.1170.1150.1130.1110.1200.1180.1150.1130.1110.0000.0460.0460.0460.0470.0470.0480.0000.0140.1540.1550.1540.1540.1540.0280.0170.0160.0260.0250.0250.1551.000-0.005-0.005-0.006-0.006-0.006-0.0060.3550.4750.3500.4640.4750.3540.3520.0850.0940.1030.1120.1210.1300.0770.0680.0590.0500.041
temp-0.0390.2150.2150.2150.2150.2160.2160.2150.2150.2150.2150.215-0.0050.0030.0030.0030.0030.0030.0030.0030.0030.0020.0020.0030.203-0.455-0.448-0.442-0.435-0.423-0.4110.0010.7810.3020.3100.3180.3190.322-0.014-0.014-0.013-0.013-0.013-0.0130.294-0.0051.0000.9960.9930.9890.9820.9750.0670.0530.0330.1240.0410.1140.0800.1030.1030.1030.1040.1040.1040.1020.1020.1020.1020.102
temp_lag_1-0.0390.2150.2150.2150.2150.2150.2150.2150.2150.2150.2150.215-0.0050.0020.0020.0030.0030.0030.0030.0020.0020.0020.0020.0020.208-0.449-0.455-0.448-0.442-0.435-0.4230.0010.7810.2940.3020.3100.3180.319-0.011-0.013-0.013-0.013-0.013-0.0130.278-0.0050.9961.0000.9960.9930.9890.9820.0670.0550.0340.1230.0420.1130.0820.1020.1030.1030.1030.1030.1040.1020.1020.1020.1020.102
temp_lag_2-0.0390.2150.2150.2150.2150.2150.2150.2150.2150.2150.2150.215-0.0060.0020.0020.0020.0020.0020.0030.0020.0020.0020.0020.0020.213-0.444-0.449-0.455-0.448-0.442-0.4350.0010.7810.2780.2940.3020.3100.317-0.008-0.011-0.013-0.013-0.013-0.0130.265-0.0060.9930.9961.0000.9960.9930.9890.0660.0570.0350.1230.0430.1130.0840.1020.1020.1030.1030.1030.1030.1020.1020.1020.1020.100
temp_lag_3-0.0400.2150.2150.2150.2150.2150.2150.2150.2150.2150.2150.215-0.0060.0020.0020.0020.0020.0020.0020.0020.0020.0020.0020.0020.218-0.438-0.444-0.449-0.455-0.448-0.4420.0010.7810.2650.2780.2940.3010.309-0.005-0.008-0.011-0.013-0.013-0.0130.251-0.0060.9890.9930.9961.0000.9960.9930.0660.0600.0370.1220.0440.1130.0860.1020.1020.1020.1020.1030.1030.1020.1020.1020.1000.099
temp_lag_4-0.0400.2150.2150.2150.2150.2150.2150.2150.2150.2150.2150.215-0.0060.0010.0010.0010.0010.0020.0020.0010.0010.0010.0010.0010.219-0.427-0.438-0.444-0.449-0.455-0.4480.0010.7810.2510.2650.2780.2940.301-0.002-0.005-0.008-0.010-0.013-0.0130.229-0.0060.9820.9890.9930.9961.0000.9960.0660.0610.0380.1230.0440.1130.0880.1020.1020.1020.1020.1020.1030.1020.1020.1000.0990.098
temp_lag_5-0.0400.2150.2150.2150.2150.2150.2150.2150.2150.2150.2150.215-0.0060.0010.0010.0010.0010.0010.0010.0010.0010.0010.0010.0010.219-0.415-0.427-0.438-0.444-0.449-0.4550.0010.7810.2290.2510.2650.2780.2930.001-0.002-0.005-0.008-0.010-0.0130.212-0.0060.9750.9820.9890.9930.9961.0000.0660.0620.0400.1240.0450.1120.0900.1020.1020.1020.1020.1020.1020.1020.1000.0990.0980.096
weekday_Friday0.0760.0150.0140.0140.0130.0130.0120.0160.0170.0180.0190.0200.3550.0140.0150.0160.0170.0180.0180.0130.0130.0120.0110.0100.0000.0870.0870.0880.0880.0880.0880.0000.0350.0610.0610.0610.0610.0610.0080.0000.0000.0010.0000.0000.0610.3550.0670.0670.0660.0660.0660.0661.0000.1680.1660.1650.1690.1680.1670.2560.2560.2560.2560.2560.2560.2440.2310.2180.2050.193
weekday_Monday0.0860.0570.0550.0540.0520.0500.0490.0570.0570.0570.0570.0570.4750.0740.0740.0730.0730.0730.0730.0710.0690.0670.0640.0620.0000.0930.0930.0930.0930.0950.0960.0000.0360.0650.0660.0650.0660.0660.0150.0150.0150.0150.0150.0150.0660.4750.0530.0550.0570.0600.0610.0620.1681.0000.1660.1640.1680.1680.1670.2560.2430.2310.2180.2060.1940.2560.2560.2560.2560.256
weekday_Saturday0.0580.0750.0740.0730.0720.0710.0700.0750.0750.0750.0740.0740.3500.0410.0400.0390.0380.0380.0370.0400.0390.0380.0370.0360.0000.1280.1290.1310.1320.1340.1350.0000.0430.2340.2340.2340.2350.2350.0420.0310.0240.0230.0210.0190.2350.3500.0330.0340.0350.0370.0380.0400.1660.1661.0000.1620.1660.1660.1650.6500.6370.6240.6120.5990.5860.6500.6500.6500.6500.649
weekday_Sunday0.0980.0600.0610.0610.0610.0610.0610.0590.0570.0550.0540.0520.4640.0170.0160.0140.0130.0120.0110.0170.0170.0170.0170.0170.0000.0760.0760.0760.0760.0780.0800.0000.0570.2190.2190.2190.2190.2190.0170.0180.0280.0400.0400.0400.2190.4640.1240.1230.1230.1220.1230.1240.1650.1640.1621.0000.1640.1640.1630.6430.6440.6440.6440.6440.6440.6310.6180.6060.5930.580
weekday_Thursday0.0980.0180.0180.0170.0170.0170.0170.0170.0170.0160.0160.0150.4750.0780.0760.0740.0720.0700.0680.0770.0760.0760.0750.0740.0000.1260.1260.1260.1270.1270.1270.0000.0440.0430.0430.0430.0430.0420.0460.0430.0450.0510.0510.0520.0420.4750.0410.0420.0430.0440.0440.0450.1690.1680.1660.1641.0000.1680.1670.2560.2560.2560.2560.2560.2560.2560.2560.2560.2560.256
weekday_Tuesday0.0460.0570.0570.0570.0570.0570.0570.0560.0560.0560.0550.0550.3540.1040.1020.0990.0970.0940.0920.1030.1020.1020.1010.1000.0000.1200.1200.1210.1220.1220.1230.0000.0380.0690.0690.0690.0690.0690.0000.0000.0000.0000.0000.0000.0690.3540.1140.1130.1130.1130.1130.1120.1680.1680.1660.1640.1681.0000.1670.2550.2550.2550.2550.2550.2550.2550.2560.2560.2560.256
weekday_Wednesday0.0780.0150.0150.0160.0160.0170.0170.0150.0150.0150.0150.0150.3520.0420.0430.0440.0440.0450.0460.0400.0380.0360.0340.0320.0000.0670.0690.0710.0720.0730.0740.0000.0420.0570.0570.0570.0570.0570.0360.0210.0000.0000.0000.0000.0570.3520.0800.0820.0840.0860.0880.0900.1670.1670.1650.1630.1670.1671.0000.2540.2540.2540.2540.2540.2540.2540.2540.2540.2540.254
workday0.0640.1050.1050.1040.1030.1030.1020.1040.1030.1010.1000.0990.0850.0150.0150.0160.0160.0160.0160.0140.0130.0120.0110.0100.0000.1080.1100.1120.1140.1160.1180.0000.0790.2060.2060.2060.2060.2070.0170.0170.0170.0170.0170.0170.2060.0850.1030.1020.1020.1020.1020.1020.2560.2560.6500.6430.2560.2550.2541.0000.9900.9800.9700.9610.9510.9900.9800.9700.9610.951
workday_lag_10.0630.1040.1050.1040.1040.1030.1020.1020.1010.1000.0980.0970.0940.0140.0150.0160.0160.0160.0160.0130.0120.0110.0100.0090.0000.1070.1090.1100.1120.1140.1160.0000.0790.2050.2060.2060.2060.2070.0170.0170.0170.0170.0170.0170.2060.0940.1030.1030.1020.1020.1020.1020.2560.2430.6370.6440.2560.2550.2540.9901.0000.9900.9800.9700.9610.9800.9700.9610.9510.941
workday_lag_20.0620.1020.1040.1050.1040.1030.1030.1010.0990.0980.0970.0950.1030.0140.0150.0160.0160.0160.0160.0130.0120.0110.0090.0080.0000.1050.1070.1090.1100.1120.1140.0000.0780.2050.2060.2060.2060.2060.0170.0170.0170.0170.0170.0170.2060.1030.1030.1030.1030.1020.1020.1020.2560.2310.6240.6440.2560.2550.2540.9800.9901.0000.9900.9800.9700.9700.9610.9510.9410.931
workday_lag_30.0610.1010.1020.1030.1050.1040.1030.0990.0980.0970.0950.0940.1120.0130.0140.0150.0160.0160.0160.0120.0110.0100.0080.0070.0000.1040.1050.1070.1090.1100.1120.0000.0780.2050.2060.2060.2060.2060.0170.0170.0170.0170.0170.0170.2060.1120.1040.1030.1030.1020.1020.1020.2560.2180.6120.6440.2560.2550.2540.9700.9800.9901.0000.9900.9800.9610.9510.9410.9310.921
workday_lag_40.0600.0990.1000.1020.1030.1040.1040.0980.0960.0950.0940.0920.1210.0120.0130.0140.0150.0160.0160.0110.0100.0090.0070.0060.0000.1020.1040.1050.1070.1090.1100.0000.0770.2050.2060.2060.2060.2060.0170.0170.0170.0170.0170.0170.2060.1210.1040.1030.1030.1030.1020.1020.2560.2060.5990.6440.2560.2550.2540.9610.9700.9800.9901.0000.9900.9510.9410.9310.9210.911
workday_lag_50.0600.0980.0990.1000.1020.1030.1040.0960.0950.0940.0920.0910.1300.0110.0120.0130.0140.0150.0160.0100.0090.0080.0060.0050.0000.1000.1020.1040.1050.1070.1090.0000.0770.2050.2060.2060.2060.2060.0170.0170.0170.0170.0170.0170.2060.1300.1040.1040.1030.1030.1030.1020.2560.1940.5860.6440.2560.2550.2540.9510.9610.9700.9800.9901.0000.9410.9310.9210.9110.902
workday_lead_10.0630.1050.1040.1030.1030.1020.1010.1050.1040.1030.1010.1000.0770.0150.0160.0160.0160.0160.0160.0150.0140.0130.0120.0110.0000.1100.1120.1140.1160.1180.1210.0000.0780.2060.2060.2060.2060.2060.0170.0170.0170.0170.0170.0160.2060.0770.1020.1020.1020.1020.1020.1020.2440.2560.6500.6310.2560.2550.2540.9900.9800.9700.9610.9510.9411.0000.9900.9800.9700.961
workday_lead_20.0620.1040.1040.1030.1020.1020.1010.1050.1060.1040.1030.1020.0680.0150.0160.0160.0160.0160.0160.0150.0150.0140.0130.0120.0000.1120.1140.1160.1180.1210.1210.0000.0780.2060.2060.2060.2060.2060.0170.0170.0170.0170.0160.0160.2060.0680.1020.1020.1020.1020.1020.1000.2310.2560.6500.6180.2560.2560.2540.9800.9700.9610.9510.9410.9310.9901.0000.9900.9800.970
workday_lead_30.0620.1040.1030.1020.1020.1010.1000.1040.1050.1060.1040.1030.0590.0150.0160.0160.0160.0160.0160.0150.0150.0150.0140.0130.0000.1130.1160.1180.1200.1210.1220.0000.0770.2060.2060.2060.2060.2060.0170.0170.0170.0160.0160.0160.2060.0590.1020.1020.1020.1020.1000.0990.2180.2560.6500.6060.2560.2560.2540.9700.9610.9510.9410.9310.9210.9800.9901.0000.9900.980
workday_lead_40.0610.1030.1030.1020.1010.1010.1000.1040.1050.1050.1060.1050.0500.0150.0160.0160.0160.0160.0160.0150.0150.0150.0150.0140.0000.1150.1180.1200.1210.1220.1230.0000.0760.2060.2060.2060.2050.2050.0170.0170.0160.0160.0160.0160.2060.0500.1020.1020.1020.1000.0990.0980.2050.2560.6500.5930.2560.2560.2540.9610.9510.9410.9310.9210.9110.9700.9800.9901.0000.990
workday_lead_50.0610.1030.1020.1010.1010.1000.0990.1030.1040.1050.1050.1060.0410.0160.0160.0160.0160.0160.0170.0150.0150.0150.0150.0140.0000.1180.1200.1210.1220.1230.1230.0000.0750.2060.2060.2050.2050.2040.0170.0160.0160.0160.0160.0180.2060.0410.1020.1020.1000.0990.0980.0960.1930.2560.6490.5800.2560.2560.2540.9510.9410.9310.9210.9110.9020.9610.9700.9800.9901.000

Missing values

2024-07-14T18:20:53.438571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-14T18:20:54.196574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

holidayworkdaygreen_marketspecialities_marketeventprecippedestrians_count_lag_1pedestrians_count_lag_2pedestrians_count_lag_3pedestrians_count_lag_4pedestrians_count_lag_5event_lag_1holiday_lag_1workday_lag_1event_lag_2holiday_lag_2workday_lag_2event_lag_3holiday_lag_3workday_lag_3event_lag_4holiday_lag_4workday_lag_4event_lag_5holiday_lag_5workday_lag_5event_lead_1holiday_lead_1workday_lead_1event_lead_2holiday_lead_2workday_lead_2event_lead_3holiday_lead_3workday_lead_3event_lead_4holiday_lead_4workday_lead_4event_lead_5holiday_lead_5workday_lead_5temp_lag_1humidity_lag_1precip_lag_1temp_lag_2humidity_lag_2precip_lag_2temp_lag_3humidity_lag_3precip_lag_3temp_lag_4humidity_lag_4precip_lag_4temp_lag_5humidity_lag_5precip_lag_5weekday_Fridayweekday_Mondayweekday_Saturdayweekday_Sundayweekday_Thursdayweekday_Tuesdayweekday_Wednesdayyearmonthdayhourlocation_idtemphumidityseries
03.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.6677690.0013230.466675-0.4193840.629719-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.6131900.309481-0.19875-0.6131080.309548-0.19875-0.6286020.764533-0.19875-0.6285170.764510-0.19875-0.6284330.764487-0.198750.01.00.00.00.00.00.02024111476.04.980.73697
13.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.080760-0.6678350.0012150.466651-0.419514-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.6131900.309481-0.19875-0.6131080.309548-0.19875-0.6130240.309567-0.19875-0.6285170.764510-0.19875-0.6284330.764487-0.198750.01.00.00.00.00.00.02024112135.06.275.01211
23.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.613010-0.080816-0.6679530.0011810.466529-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.410667-0.074903-0.19875-0.6131080.309548-0.19875-0.6130240.309567-0.19875-0.6129390.309549-0.19875-0.6284330.764487-0.198750.01.00.00.00.00.00.02024112470.06.275.01118
33.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.714861-0.613075-0.080925-0.6680000.001055-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.410667-0.074903-0.19875-0.410588-0.074849-0.19875-0.6130240.309567-0.19875-0.6129390.309549-0.19875-0.6128550.309531-0.198750.01.00.00.00.00.00.02024112476.06.275.01243
43.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.577965-0.714928-0.613192-0.080960-0.668132-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.410667-0.074903-0.19875-0.410588-0.074849-0.19875-0.410507-0.074835-0.19875-0.6129390.309549-0.19875-0.6128550.309531-0.198750.01.00.00.00.00.00.02024113135.06.275.55173
53.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.654627-0.578029-0.715046-0.613239-0.081087-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.410667-0.038615-0.19875-0.410588-0.074849-0.19875-0.410507-0.074835-0.19875-0.410426-0.074849-0.19875-0.6128550.309531-0.198750.01.00.00.00.00.00.02024113470.06.275.5587
63.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.748811-0.654692-0.578146-0.715094-0.613370-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.410667-0.038615-0.19875-0.410588-0.038560-0.19875-0.410507-0.074835-0.19875-0.410426-0.074849-0.19875-0.410345-0.074863-0.198750.01.00.00.00.00.00.02024113476.06.275.55214
73.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.609725-0.748878-0.654810-0.578191-0.715227-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.410667-0.038615-0.19875-0.410588-0.038560-0.19875-0.410507-0.038545-0.19875-0.410426-0.074849-0.19875-0.410345-0.074863-0.198750.01.00.00.00.00.00.02024114135.05.779.25174
83.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.653531-0.609790-0.748997-0.654857-0.578323-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.4885600.210025-0.19875-0.410588-0.038560-0.19875-0.410507-0.038545-0.19875-0.410426-0.038560-0.19875-0.410345-0.074863-0.198750.01.00.00.00.00.00.02024114470.05.779.2595
93.4622460.628236-1.147189-1.147189-0.430474-0.19875-0.740050-0.653597-0.609907-0.749046-0.654989-0.4303273.4600570.628236-0.430183.4578720.628236-0.4300333.455690.628236-0.4298863.4535120.628236-0.429743.4513380.628236-0.430623.4644390.628236-0.4307673.4666360.628236-0.4309143.4688360.628236-0.4310613.471040.628236-0.4312073.4732480.628236-0.4885600.210025-0.19875-0.4884800.210089-0.19875-0.410507-0.038545-0.19875-0.410426-0.038560-0.19875-0.410345-0.038574-0.198750.01.00.00.00.00.00.02024114476.05.779.25238
holidayworkdaygreen_marketspecialities_marketeventprecippedestrians_count_lag_1pedestrians_count_lag_2pedestrians_count_lag_3pedestrians_count_lag_4pedestrians_count_lag_5event_lag_1holiday_lag_1workday_lag_1event_lag_2holiday_lag_2workday_lag_2event_lag_3holiday_lag_3workday_lag_3event_lag_4holiday_lag_4workday_lag_4event_lag_5holiday_lag_5workday_lag_5event_lead_1holiday_lead_1workday_lead_1event_lead_2holiday_lead_2workday_lead_2event_lead_3holiday_lead_3workday_lead_3event_lead_4holiday_lead_4workday_lead_4event_lead_5holiday_lead_5workday_lead_5temp_lag_1humidity_lag_1precip_lag_1temp_lag_2humidity_lag_2precip_lag_2temp_lag_3humidity_lag_3precip_lag_3temp_lag_4humidity_lag_4precip_lag_4temp_lag_5humidity_lag_5precip_lag_5weekday_Fridayweekday_Mondayweekday_Saturdayweekday_Sundayweekday_Thursdayweekday_Tuesdayweekday_Wednesdayyearmonthdayhourlocation_idtemphumidityseries
13887-0.28883-1.5917580.8716960.8716962.323022-0.19875-0.691862-0.762020-0.780758-0.719475-0.7667032.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917580.9602580.412297-0.198750.9603170.412368-0.198750.9603750.412388-0.198750.9604340.786687-0.198750.9604930.786664-0.198750.00.01.00.00.00.00.020247138135.016.077.15223
13888-0.28883-1.5917580.8716960.8716962.323022-0.19875-0.599868-0.691929-0.762140-0.780808-0.7196082.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.1160450.068905-0.198750.9603170.412368-0.198750.9603750.412388-0.198750.9604340.412369-0.198750.9604930.786664-0.198750.00.01.00.00.00.00.020247138470.016.077.15208
13889-0.28883-1.5917580.8716960.8716962.323022-0.19875-0.616296-0.599933-0.692047-0.762189-0.7809412.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.1160450.068905-0.198751.1161010.068964-0.198750.9603750.412388-0.198750.9604340.412369-0.198750.9604930.412350-0.198750.00.01.00.00.00.00.020247138476.016.077.15375
13890-0.28883-1.5917580.8716960.8716962.323022-0.19875-0.433403-0.616361-0.600050-0.692095-0.7623222.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.1160450.068905-0.198751.1161010.068964-0.198751.1161580.068980-0.198750.9604340.412369-0.198750.9604930.412350-0.198750.00.01.00.00.00.00.020247139135.017.072.39928
13891-0.28883-1.5917580.8716960.8716962.323022-0.198750.172224-0.433465-0.616478-0.600096-0.6922272.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.271832-0.250967-0.198751.1161010.068964-0.198751.1161580.068980-0.198751.1162140.068964-0.198750.9604930.412350-0.198750.00.01.00.00.00.00.020247139470.017.072.39513
13892-0.28883-1.5917580.8716960.8716962.323022-0.19875-0.2822700.172172-0.433580-0.616524-0.6002282.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.271832-0.250967-0.198751.271886-0.250919-0.198751.1161580.068980-0.198751.1162140.068964-0.198751.1162700.068949-0.198750.00.01.00.00.00.00.020247139476.017.072.39756
13893-0.28883-1.5917580.8716960.8716962.323022-0.19875-0.016145-0.2823300.172066-0.433622-0.6166562.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.271832-0.250967-0.198751.271886-0.250919-0.198751.271940-0.250907-0.198751.1162140.068964-0.198751.1162700.068949-0.198750.00.01.00.00.00.00.0202471310135.017.067.761856
13894-0.28883-1.5917580.8716960.8716962.323022-0.198751.188538-0.016200-0.2824420.172036-0.4337522.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.271832-0.562103-0.198751.271886-0.250919-0.198751.271940-0.250907-0.198751.271993-0.250920-0.198751.1162700.068949-0.198750.00.01.00.00.00.00.0202471310470.017.067.761027
13895-0.28883-1.5917580.8716960.8716962.323022-0.198750.2806451.188503-0.016308-0.2824810.1719112.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.271832-0.562103-0.198751.271886-0.562066-0.198751.271940-0.250907-0.198751.271993-0.250920-0.198751.272047-0.250932-0.198750.00.01.00.00.00.00.0202471310476.017.067.761287
13896-0.28883-1.5917580.8716960.8716962.323022-0.198750.5653880.2805951.188412-0.016342-0.2826102.323815-0.289013-1.5917582.324608-0.289195-1.5917582.325402-0.289378-1.5917582.326196-0.28956-1.5917582.326991-0.289743-1.5917582.32223-0.288647-1.5917582.321439-0.288464-1.5917582.320649-0.288281-1.5917582.319859-0.288098-1.5917582.31907-0.287915-1.5917581.271832-0.562103-0.198751.271886-0.562066-0.198751.271940-0.562058-0.198751.271993-0.250920-0.198751.272047-0.250932-0.198750.00.01.00.00.00.00.0202471311135.018.063.613309